The Trusted AI Bulletin #7
11/04/2025Regulation,Strategy,Investment Banking,Wealth Management,Data & Analytics,Artificial Intelligence,Newsletter,Risk,Data,Governance,PeopleInsurance
Issue #7 – Scaling AI Responsibly: From Compliance to Competitive Edge
Introduction
In this edition of The Trusted AI Bulletin, we examine the shifting centre of gravity for AI within financial services firms. The newly released 2025 AI Index Report highlights not only the global acceleration in AI development but also the widening gaps in regulatory preparedness and organisational readiness.
Our Co-Founder Thushan Kumaraswamy opens with a perspective on the need for business-led AI ownership — a view increasingly echoed across the industry. As firms move from experimentation to enterprise adoption, clarity around governance, accountability, and value realisation is no longer optional. This issue explores what that shift looks like in practice.
Executive Perspective: Where Should AI Responsibility Live?
Our Co-Founder Thushan Kumaraswamy comments:
"The 2025 AI Index Report raise some interesting challenges regarding Responsible AI (RAI) in business. The use of AI in financial services firms requires cooperation between multiple departments, but ownership of AI remains fragmented. Currently, it sits with information security or data teams, but it really needs to be owned by the business.
It is the business that is paying for the AI systems to be developed and adopted. It is the business that owns the data used by the AI systems. It is the business that (hopefully!) sees the value realised.
I am starting to see more “Head of Responsible AI” noise in financial services firms now with Lloyds Banking Group hiring in Jan 2025, but still not that many and it remains unclear if these kinds of roles are data/tech-related or part of the business.
I get that AI, both from a technical perspective and an operational one, is new for many business leaders, and they struggle to keep up with the daily barrage of innovations. This is where a “Head of AI” should sit; to advise the business on what is possible with AI, to work with data, technology, and infosec teams to ensure that AI systems are used safely, and to ensure that the ROI of AI is at least what is expected.
Specialists can advise on a temporary basis, but in the long-term this must be an in-house role and team, supported by the board, and given the necessary authority to stop AI developments at any stage if they pose uncontrolled risks to the firm or will not deliver the required return."
100 AI Use Cases in Financial Services - #1 Chatbot
In last week’s edition, we introduced our series on the 100 AI use cases reshaping financial services — focusing on how firms can move from experimentation to scalable, high-impact adoption.
We kick off the series with one of the most widely adopted and visible AI use cases: chatbots. In financial services, chatbots are transforming how firms interact with customers — delivering faster support, tailored advice, and improved satisfaction. But the benefits come with real risks around data privacy, regulatory compliance, and fairness.
AI Highlights of the Week
1. New AI Index Report Charts Rising Global Stakes—and Regulatory Gaps
The 2025 AI Index Report has just been released by Stanford University, offering a comprehensive snapshot of global trends in artificial intelligence. This year’s report underscores the intensifying race between nations, with the United States still leading in the development of top AI models but China rapidly catching up, especially in research output and patent filings.
The report draws attention to the soaring cost of training cutting-edge models—OpenAI’s GPT-4 is estimated to have cost $78 million—raising questions about who can afford to innovate at this scale. Notably, AI regulation is on the rise: U.S. AI-related laws have grown from just one in 2016 to 25 in 2023, reflecting the increasing pressure on governments to keep pace with technological advancement.
As AI systems become more powerful and embedded in daily life, the findings stress the urgent need for thoughtful, coordinated governance that can balance innovation with accountability. With AI's trajectory showing no signs of slowing, the report serves as a timely reminder that regulatory frameworks must evolve just as swiftly.
Source: 2025 AI Index Report link
2. Brussels Bets Big on AI to Regain Tech Edge and Counter U.S. Tariffs
As the European Union grapples with the ripple effects of American tariffs, Brussels is preparing a major policy shift aimed at transforming Europe into an “AI Continent.” A draft strategy, to be unveiled this week, reveals plans to streamline regulations, reduce compliance burdens, and create a more innovation-friendly environment for AI development.
This charm offensive is a direct response to mounting criticism from Big Tech and global AI leaders, who argue that the EU’s rigid regulatory framework, including the AI Act, is stifling competitiveness. Central to the strategy are massive investments in computing infrastructure — including five AI “gigafactories” — and ambitious targets to boost AI skills among 100 million Europeans by 2030.
The push also seeks to reduce dependence on U.S.-based cloud providers by tripling Europe’s data centre capacity. With only 13 percent of European firms currently adopting AI, the plan signals a timely recalibration of Europe's approach to AI governance — one that recognises the urgent need to lead, not lag, in the global AI race.
Source: Politico link
3. Standard Chartered Embraces Generative AI to Revolutionise Global Operations
Standard Chartered is set to deploy its Generative AI tool, SC GPT, across 41 markets, aiming to enhance operational efficiency and client engagement among its 70,000 employees. This strategic move is expected to boost productivity, personalise sales and marketing efforts, automate software engineering tasks, and refine risk management processes.
A more tailored version is in development to leverage the bank's proprietary data for bespoke problem-solving, while local teams are encouraged to adapt SC GPT to address specific market needs, including digital marketing and customer services. This initiative underscores Standard Chartered's commitment to responsibly harnessing AI, reflecting a broader trend in the financial sector towards integrating advanced technologies.
As AI governance and regulations evolve, such proactive adoption highlights the importance of balancing innovation with ethical considerations in the banking industry.
Source: Finextra link
4. Navigating the AI Revolution: Ensuring Responsible Innovation in UK Financial Services
The integration of AI into financial services is revolutionising the sector, enhancing operations from algorithmic trading to personalised customer interactions. However, this rapid adoption introduces significant regulatory challenges, particularly concerning financial stability and consumer protection.
The UK's Financial Conduct Authority (FCA) has yet to implement comprehensive AI regulations, leading to ambiguity in compliance and oversight. Unregulated AI-driven activities, such as algorithmic trading, could exacerbate market volatility, while biased AI models in credit scoring may disadvantage vulnerable consumers.
To address these issues, financial institutions should proactively enhance AI governance frameworks, prioritising transparency, bias mitigation, and robust cybersecurity measures. Engaging with policymakers to establish clear, forward-thinking regulations is crucial to balance innovation with economic stability.
As AI continues to redefine financial services, the UK's ability to implement effective governance will determine its leadership in this evolving landscape.
Source: HM Strategy link
Industry Insights
Case study – Allianz Scaling Responsible AI Across Global Insurance Operations
Allianz, one of the world’s largest insurers, is taking a leading role in translating Responsible AI principles into real-world practice across its global operations. With nearly 160,000 employees and a presence in more than 70 countries, the company has moved beyond AI experimentation to embed ethical safeguards into scalable AI deployment.
In 2024, Allianz joined the European Commission’s AI Pact, aligning its roadmap with the EU AI Act and signalling its intent to not just comply, but lead on AI governance.
At the core of Allianz’s approach is a practical, organisation-wide AI Risk Management Framework developed in-house. This framework governs all AI and machine learning initiatives, from document processing to customer service automation, with defined roles for model owners, risk teams, and compliance functions.
Key initiatives include:
- An AI Impact Assessment Tool used early in development to flag risks such as discriminatory outcomes, low explainability, or overreliance on sensitive data.
- The Enterprise Knowledge Assistant (EKA), a GenAI-powered tool now used by thousands of service agents to cut resolution times and improve consistency across 10+ countries.
- A strict model registration process and “human-in-the-loop” policy to ensure that critical decisions — like claims rejection or fraud detection — are always overseen by a human.
- Mandatory training for AI Product Owners, with oversight from a central AI governance board embedded in Group Compliance.
These measures are not theoretical. They have enabled Allianz to scale nearly 400 GenAI use cases while maintaining regulatory confidence, internal accountability, and public trust. For Allianz, AI governance is more than risk mitigation — it’s what allows innovation to scale responsibly, without compromising on customer fairness or institutional integrity.
Sources: Allianz link 1, Allianz link 2, WE Forum PDF link
Upcoming Events
1. Gartner Expert Q&A: Practical Guidance on Adapting to the EU AI Act -14 April 2025
This webinar offers valuable insights for businesses navigating the new EU AI regulations. Industry experts will provide actionable advice on how to ensure compliance and unlock opportunities within the evolving AI landscape. It's a must-attend for anyone keen to stay ahead of regulatory changes and ensure their AI strategies are future-proof.
Register now: Gartner link
2. In-Person Event: AI Breakfast & Roundtable – From AI Proof of Concept to Scalable Enterprise Adoption – 23 April 2025
Leading Point is hosting an exclusive AI Breakfast & Roundtable, bringing together AI leaders from top financial institutions, including banks, insurance firms, and buy-side institutions. This intimate, high-level discussion will explore the challenges and opportunities in scaling AI beyond proof of concept to enterprise-wide adoption.
Key discussion points include overcoming implementation barriers, aligning AI initiatives with business objectives, and best practices for AI success in banking, insurance, and investment management. This event offers a unique opportunity to connect with industry peers and gain strategic insights on embedding AI as a core driver of business value.
Want to be a part of the conversation?
If you are an executive with AI responsibilities in business, risk & compliance, or data contact Rajen Madan or Thushan Kumaraswamy to get a seat at the table.
3. In-Person Event: The AI in Business Conference - 15 May 2025
This in-person event offers a unique opportunity to hear from industry leaders across various sectors, providing real-world insights into AI implementation and strategy. Attendees will benefit from a rich agenda of expert sessions and have the chance to network with like-minded professionals, building lasting connections while tackling common challenges in AI.
Plus, the event is co-located with the Digital Transformation Conference, allowing platinum ticket holders to access a broader range of content, deepening their understanding of AI’s role in digital business transformation.
Register now: AI Business Conference link
Conclusion
The themes emerging across this issue point to a maturing AI agenda in financial services: from clearer governance models and responsible scaling, to regulatory recalibration and infrastructure investment. What’s clear is that AI can no longer be treated as a peripheral capability — it must be embedded within core business strategy, with the right controls in place from the outset.
As organisations seek to balance innovation with oversight, the ability to operationalise Responsible AI at scale will define not only compliance readiness but also competitive advantage.
In our next issue, we’ll continue the ‘100 AI Use Cases’ series with a focus on AI in Investment Research — examining how firms are using AI to enhance insight generation, improve analyst productivity, and navigate the risks of model-driven decision-making.
The Trusted AI Bulletin #6
28/03/2025Regulation,Strategy,Investment Banking,Wealth Management,Data & Analytics,Artificial Intelligence,Newsletter,Risk,Data,Governance,PeopleInsurance
Issue #6 – The AI Policy Pulse: Balancing Risk, Trust & Progress
Introduction
Welcome to this edition of The Trusted AI Bulletin, where we break down the latest shifts in AI policy, regulation, and the evolving landscape of AI adoption.
This week, we explore the debate over AI risk assessments in the EU, as MEPs push back against a proposal that could exempt major tech firms from stricter oversight. We also examine the UK’s latest strategy for regulating AI in financial services and how businesses are navigating the complexities of AI adoption—balancing innovation with compliance. Meanwhile, in government, outdated infrastructure threatens to stall progress, underscoring the need for practical transformation strategies.
With AI becoming ever more embedded in critical systems, the focus is shifting to how organisations can create real value with AI while ensuring responsible governance. From regulatory battles to real-world implementation challenges, these stories highlight the urgent need for a balanced approach—one that drives adoption, fuels transformation, and keeps accountability at its core.
100 AI Use Cases in Financial Services
AI adoption in financial services is no longer a question of if, but where and how. As firms move beyond experimentation, the focus is shifting toward practical, high impact use cases that can drive real operational and strategic value. From front-office customer engagement to back-office automation, the opportunities to embed AI across the business are expanding rapidly.
But with so many possibilities, the challenge lies in identifying where AI can deliver meaningful outcomes — and doing so in a way that’s scalable, compliant, and aligned with the firm’s broader objectives. That’s where a clear view of proven, emerging use cases becomes essential.
Over the coming weeks, we’ll be exploring the 100 AI Use Cases we have identified shaping the future of financial services. For each, we’ll look at the models involved, the data required, key vendors operating in the space, risk considerations, and examples of where adoption is already underway. The goal is to help senior leaders cut through the noise and focus on the AI opportunities that matter — now and next.
Key Highlights of the Week
1. MEPs Criticise EU's Shift Towards Voluntary AI Risk Assessments
A coalition of Members of the European Parliament (MEPs) has expressed significant concern over the European Commission's proposal to make certain AI risk assessment provisions voluntary, particularly those affecting general-purpose AI systems like ChatGPT and Copilot.
This move could exempt major tech companies from mandatory evaluations of their systems for issues such as discrimination or election interference. The MEPs argue that such a change undermines the AI Act's foundational goals of safeguarding fundamental rights and democracy.
This development highlights the ongoing tension between regulatory bodies and technology firms, especially from the United States, regarding the balance between innovation and ethical oversight in AI governance. The outcome of this debate will be pivotal in shaping the future landscape of AI regulation within the European Union.
Source: Dutch News link
2. UK Financial Regulator Launches Strategy to Balance Risk and Foster Economic Growth
The FCA has launched a new five-year strategy focused on boosting trust, supporting innovation, and improving outcomes for consumers across UK financial services.
By committing to becoming a more data-led, tech-savvy regulator, the FCA aims to strike a better balance between risk and growth—an approach that holds significant implications for the governance of emerging technologies like AI.
Its emphasis on smarter regulation, financial crime prevention, and inclusive consumer support signals a shift toward more agile, forward-looking oversight. For those navigating evolving AI regulations, this strategy reinforces the FCA’s intent to create a regulatory environment that fosters responsible innovation.
Source: FCA link
3. Public Accounts Committee Warns of AI Rollout Challenges Amid Legacy Infrastructure
The UK government's ambitious plans to integrate AI across public services are at risk due to outdated IT infrastructure, poor data quality, and a shortage of skilled personnel.
A report by the Public Accounts Committee (PAC) highlights that over 20 legacy IT systems remain unfunded for necessary upgrades, with nearly a third of central government systems deemed obsolete as of 2024.
Despite intentions to drive economic growth through AI adoption, these foundational weaknesses pose significant challenges. The PAC also raises concerns about persistent digital skills shortages and uncompetitive civil service pay rates, which hinder the recruitment and retention of necessary talent.
Addressing these issues is crucial to ensure that AI initiatives are effectively implemented, fostering public trust and delivering the anticipated benefits of technological advancement.
Source: The Guardian link
Featured Articles
1. Why the UK’s Light-Touch AI Approach Might Not Be Enough
AI regulation in the UK is developing at a cautious pace, with the government opting for a principles-based, sector-led approach rather than comprehensive legislation. While this flexible model aims to foster innovation and reduce regulatory burdens, it risks creating a fragmented landscape where inconsistent standards could undermine public trust and accountability.
The article highlights that regulators often lack the technical expertise and resources to effectively oversee AI, raising concerns about how well current frameworks can keep pace with rapid technological advancements.
Meanwhile, businesses are calling for greater clarity and coherence, especially those operating across borders and facing stricter regimes like the EU AI Act. The UK’s strategy, though well-intentioned, may fall short in addressing the systemic risks posed by AI if coordination and enforcement mechanisms remain weak. For those focused on AI governance, the message is clear: without sharper oversight and alignment, the UK could lag in both trust and competitiveness.
Source: ICAEW link
2. Bridging the AI Knowledge Gap: A Foundation for Responsible Innovation
In an era where artificial intelligence is reshaping everything from financial services to public policy, understanding how AI works is becoming essential—not just for technologists, but for everyone.
As AI systems increasingly influence the decisions we see, the products we use, and even the jobs we do, being AI-literate is no longer a nice-to-have, but a societal imperative. The CFTE AI Literacy White Paper explores why foundational knowledge of AI is critical for individuals, businesses, and governments alike, arguing that AI should be treated as a core component of digital literacy.
What’s particularly compelling is the focus on inclusion—ensuring that access to AI knowledge isn't limited to a technical elite but extended across sectors and demographics. Without widespread AI literacy, regulatory and governance efforts risk being outpaced by innovation.
This makes the paper especially relevant to those shaping or responding to emerging AI regulations and frameworks. It’s both a call to action and a roadmap for building a more informed, resilient society in the age of intelligent systems.
Source: CTFE link
3. AI in 2025: From Reasoning Machines to Multimodal Intelligence
The year ahead promises significant advances in artificial intelligence, particularly in areas like reasoning, frontier models, and multimodal capabilities. Large language models are evolving to exhibit more sophisticated forms of human-like reasoning, enhancing their utility across sectors from healthcare to finance.
At the same time, so-called frontier models—exceptionally large and powerful systems—are setting new benchmarks in tasks like image generation and complex decision-making. Multimodal AI, which integrates text, image, and audio inputs, is maturing rapidly and could redefine how machines interpret and respond to the world.
These developments underscore the urgency for updated governance frameworks that can keep pace with AI’s expanding scope and impact. As capabilities grow, so too does the need for greater regulatory clarity and ethical oversight.
Source: Morgan Stanley link
Industry Insights
Case Study: Building a Trustworthy Data Foundation for Responsible AI
Capital One, a major retail bank and credit card provider, has positioned itself at the forefront of responsible AI by investing in a robust, AI-ready data ecosystem. Operating in a highly regulated industry where trust and accuracy is vital, the company recognised early on that scalable, ethical AI requires more than just advanced algorithms—it demands a disciplined approach to data governance and transparency.
In recent years, Capital One has overhauled its data infrastructure to align with its long-term AI vision, focusing on quality, accessibility, and accountability across the entire data lifecycle.
To support this transformation, Capital One implemented a suite of Responsible AI practices, including standardised metadata tagging, active data lineage tracking, and embedded governance controls across cloud-native platforms. These efforts are supported by cross-functional teams that bring together AI researchers, compliance professionals, and data engineers to operationalise fairness, explainability, and bias mitigation.
The results are tangible: Capital One has accelerated the deployment of customer-facing AI solutions—such as fraud detection and credit risk models—while ensuring they meet internal and regulatory standards. By prioritising responsible data management as the foundation for AI, the company is not only enhancing trust with regulators and customers but also driving innovation with confidence.
Key Takeaways:
1. Data governance first: Ethical AI starts with well-governed, high-quality data.
2. Cross-functional collaboration: Aligning compliance, engineering and AI teams is key to operationalising responsibility.
3. Built-in controls, not bolt-ons: Embedding governance into AI systems from the outset enhances both trust and speed to market.
Sources: Forbes link, Capital One link
Upcoming Events
1. Webinar: D&A Leaders: Preparing Your Data for AI Integration – 2 April 2025 3:00 am BST
Gartner's upcoming webinar, "D&A Leaders, Ready Your Data for AI," focuses on equipping data and analytics professionals with strategies to prepare organisational data for effective artificial intelligence integration. The session will cover best practices for data quality management, governance frameworks, and aligning data strategies with AI objectives. Attendees will gain actionable insights to ensure their data assets are primed for AI-driven initiatives, enhancing decision-making and business outcomes.
Register now: Gartner link
2. In-Person Event: AI Breakfast & Roundtable – From AI Proof of Concept to Scalable Enterprise Adoption – 23 April 2025
Leading Point is hosting an exclusive AI Breakfast & Roundtable, bringing together AI leaders from top financial institutions, including banks, insurance firms, and buy-side institutions. This intimate, high-level discussion will explore the challenges and opportunities in scaling AI beyond proof of concept to enterprise-wide adoption.
Key discussion points include overcoming implementation barriers, aligning AI initiatives with business objectives, and best practices for AI success in banking, insurance, and investment management. This event offers a unique opportunity to connect with industry peers and gain strategic insights on embedding AI as a core driver of business value.
Want to be a part of the conversation?
If you are an executive with AI responsibilities in business, risk & compliance, or data contact Rajen Madan or Thushan Kumaraswamy to get a seat at the table.
3. In-Person Event: Smarter Couds, Stronger AI-Driven Innovation, Efficiency and Resilience – 24 April 2025
IDC’s, HPE’s and TCS’s upcoming roundtable Smarter Clouds, Stronger Businesses explores how enterprises can drive innovation and resilience by aligning AI strategies with modern cloud architectures. With a focus on agility, scalability, and performance, the agenda covers best practices for adopting AI-enabled infrastructure, building secure and future-ready cloud environments, and reducing complexity across hybrid ecosystems. Industry experts will share insights on turning cloud investments into long-term business value—enabling organisations to stay competitive in an increasingly data-driven world.
Register now: IDC link
4. In-Person Event: Risk & Compliance in Financial Services - 29 April 2025
The 9th Annual Risk & Compliance in Financial Services Conference brings together senior professionals from firms such as Aviva, Invesco, Lloyds Banking Group and NatWest. This year’s agenda focuses on emerging challenges and innovations in the sector—from the use of AI to enhance compliance and operational resilience, to navigating evolving regulations like DORA and Consumer Duty. With expert-led panels on financial crime, cyber risk, and ESG reporting, attendees can expect forward-looking insights tailored for today’s risk environment.
Register now: Financial IT link
Conclusion
The developments this week reinforce a crucial reality: effective AI governance is about more than setting rules—it’s about ensuring accountability, trust, and long-term resilience. Whether it’s the EU’s regulatory crossroads, the FCA’s push for a more agile oversight model, or the challenges of AI integration in the public sector, one thing is clear: the success of AI depends on the frameworks we build today.
As AI capabilities expand, so too must our approach to regulation, ethics, and education. The road ahead demands collaboration between policymakers, businesses, and technologists to create systems that not only foster innovation but also safeguard society.
We’ll be back in two weeks with more insights. Until then, let’s continue driving the conversation on responsible AI.
The Trusted AI Bulletin #5
14/03/2025Regulation,Strategy,Investment Banking,Wealth Management,Data & Analytics,Artificial Intelligence,Newsletter,Risk,Data,Governance,PeopleInsurance
Issue #5 – AI at the Edge: Governing the Future of Innovation
Introduction
Welcome to this week’s edition of The Trusted AI Bulletin, where we unpack the latest developments in AI governance, regulation, and adoption.
This week, we’re diving into OpenAI’s push for federal AI regulations, the launch of new compliance standards for bank-fintech partnerships, and the stark warnings from Turing Award winners about the unsafe deployment of AI models. As governments and businesses grapple with the dual demands of innovation and accountability, the conversation around responsible AI is reaching a critical inflection point.
The rapid evolution of AI is forcing a reckoning: how do we balance the need for speed and competitiveness with the imperative to build safeguards that protect society? From the financial sector’s embrace of AI-driven tools to IKEA’s leadership in ethical AI governance, the stories this week highlight both the opportunities and the risks of this transformative technology.
Key Highlights of the Week
1. OpenAI Appeals to White House for Unified AI Regulations Amidst State-Level Disparities
OpenAI has formally requested the White House to intervene against a patchwork of state-level AI regulations, advocating for a cohesive federal framework to govern artificial intelligence. This move underscores the company's concern that disparate state laws could stifle innovation and create compliance challenges.
Notably, OpenAI's Chief Global Affairs Officer, Chris Lehane, has highlighted the urgency of accelerating AI policy under the current administration, shifting from merely advocating regulation to actively promoting policies that bolster AI growth and maintain the U.S.'s competitive edge over nations like China.
In a 15-page set of policy suggestions released on Thursday, OpenAI argued that the hundreds of AI-related bills currently pending across the U.S. risk undercutting America's technological progress at a time when it faces renewed competition from China. The company proposed that the administration consider providing relief for AI companies from state rules in exchange for voluntary access to their models.
Source: Bloomberg link
2. CFES Unveils New Standards to Strengthen Compliance in Bank-Fintech Partnerships
The Coalition for Financial Ecosystem Standards (CFES) announced in a press release this week the launch of a new industry framework aimed at strengthening compliance and risk management in bank-fintech partnerships. The STARC framework, comprising 54 standards, sets a benchmark for key areas such as anti-money laundering (AML), third-party risk, and operational compliance, providing financial institutions with a structured rating system to assess their maturity.
To support adoption, CFES has also established an Advisory Board featuring key industry players like the Independent Community Bankers of America (ICBA) and the American Fintech Council (AFC). With regulators increasing scrutiny on fintech partnerships, these standards could play an important role in helping firms navigate compliance without stifling innovation.
As artificial intelligence continues to reshape financial services, frameworks like STARC offer a structured approach to ensuring transparency and accountability.
Source: Press release PDF link, CFES Standards link
3. Turing Award winners warn over unsafe deployment of AI models
AI pioneers Andrew Barto and Richard Sutton have strongly criticised the industry’s reckless approach to deploying AI models, warning that companies are prioritising speed and profit over responsible engineering. They argue that releasing untested AI systems to millions without safeguards is a dangerous practice, likening it to building a bridge and testing it by sending people across.Their work, which underpins major advancements in machine learning, has fuelled the rise of AI powerhouses such as OpenAI and Google DeepMind.
The pair, who have been awarded the 2024 Turing Award for their foundational contributions to artificial intelligence, have expressed serious concerns that AI development is being driven by business incentives rather than a focus on safety. Barto criticised the industry’s approach, stating, “Releasing software to millions of people without safeguards is not good engineering practice,” while Sutton dismissed the idea of artificial general intelligence (AGI) as mere “hype.” As AI investment reaches unprecedented levels, their warnings highlight the growing tensions between rapid technological advancement and the urgent need for stronger governance and regulatory oversight.
Source: FT link
Featured Articles
1. How Artificial Intelligence is Shaping the Future of Banking and Finance
The financial services sector is experiencing a significant transformation through the integration of artificial intelligence (AI), with investments projected to escalate from $35 billion in 2023 to $97 billion by 2027, reflecting a compound annual growth rate of 29%.
Leading institutions such as Morgan Stanley and JPMorgan Chase have introduced AI-driven tools to enhance operational efficiency and client services. In the immediate term, AI co-pilots are streamlining workflows, while always-on AI web crawlers and automation of unstructured data tasks are providing real-time insights and reducing manual processes.
Looking ahead, AI's potential to revolutionise risk management and customer experience through the use of synthetic data is becoming increasingly evident. Fintech companies are at the forefront of this evolution, democratising AI capabilities and enabling smaller financial institutions to compete effectively. This rapid AI adoption underscores the urgency for robust AI governance and regulatory frameworks to ensure ethical implementation and maintain public trust.
Source: Forbes link
2. Mandatory AI Governance: Gartner Predicts Worldwide Regulatory Adoption by 2027
According to Gartner's research, by 2027, AI governance is expected to become a mandatory component of national regulations worldwide. This projection underscores the escalating concerns surrounding data security and the imperative for robust governance frameworks in the rapidly evolving AI landscape.
Notably, Gartner anticipates that over 40% of AI-related data breaches could stem from cross-border misuse of generative AI, highlighting the critical need for cohesive ethical governance. The absence of such frameworks may result in organisations failing to realise the anticipated value of their AI initiatives.
This development signals a pivotal shift towards more stringent AI oversight, emphasising the necessity for organisations to proactively adopt comprehensive governance strategies to mitigate risks and ensure compliance with forthcoming regulatory standards.
Source: CDO Magazine link
3. Balancing Control and Collaboration: Five Essential Layers of AI Sovereignty
The concept of AI sovereignty extends far beyond data localisation or regulatory compliance, requiring a multi-layered approach to ensure true independence.
Five key layers define AI sovereignty: legal and regulatory control, resource and technical independence, operational autonomy, cognitive sovereignty over AI models and algorithms, and cultural influence in shaping public perception and ethical norms. Each layer plays a crucial role in balancing national or organisational control with global collaboration, ensuring AI aligns with strategic interests while maintaining adaptability.
Without a structured approach to sovereignty, reliance on external AI infrastructure and governance could pose significant risks to security, competitiveness, and ethical oversight. As AI regulations evolve, this framework highlights the need for a proactive, layered strategy to navigate the complexities of AI governance effectively.
Source: Anthony Butler link
Industry Insights
Case Study: IKEA’s responsible AI governance
As AI becomes increasingly embedded in business operations, IKEA has taken a proactive and structured approach to AI governance, ensuring ethical and responsible deployment. Recognising the potential risks of AI alongside its benefits, IKEA introduced its first digital ethics policy in 2019, laying the foundation for responsible AI development.
By 2021, the company had established a dedicated AI governance framework, with a multidisciplinary team overseeing compliance, risk management, and ethical considerations. This governance model ensures that AI is used transparently, fairly, and in alignment with business goals.
Key areas of focus include enhancing employee productivity, optimising supply chains, and improving customer experiences—all while maintaining strict ethical standards. Additionally, IKEA’s AI literacy programme is designed to empower employees with the skills needed to navigate AI responsibly, reinforcing the company’s commitment to human-centric innovation.
Key Takeaways:
1. AI Governance as a Business Imperative: Rather than treating AI governance as a regulatory checkbox, IKEA integrates responsible AI principles into its core business strategy. This ensures that AI-driven innovations align with ethical considerations and organisational priorities.
2. Proactive Regulatory Compliance: IKEA’s commitment to responsible AI extends to early compliance with the EU AI Act. As a signatory of the AI Pact, the company is ahead of regulatory requirements, demonstrating leadership in ethical AI governance.
3. Empowering Employees Through AI Education: Understanding that responsible AI usage starts with people, IKEA has launched an AI literacy programme to train 30,000 employees in 2024. This initiative fosters a culture of accountability and awareness, reducing risks associated with AI adoption.
By prioritising governance, education, and ethical AI integration, IKEA is setting a benchmark for responsible AI adoption in the retail sector, ensuring that technological advancements serve both business needs and societal good.
Sources: CIO Dive ink, Global Loyalty Organisation link
Upcoming Events
1. In-Person Event: AI for CFOs - Minimise Risk to Maximise Returns - 25 March 2025
On March 25th, 2025, The Economist is hosting the AI for CFOs event in London, focusing on how finance leaders can leverage artificial intelligence to enhance corporate performance. Attendees will explore AI's role in delivering real-time insights, improving forecasting accuracy, automating compliance, and strengthening data security. This event offers a valuable opportunity to connect with industry experts and discover actionable strategies for integrating AI into financial operations.
Register now: The Economist link
2. Webinar: Strategies and Solutions for Unlocking Value from Unstructured Data - 27 March 2025
A-Team Insight’s upcoming webinar, Strategies and Solutions for Unlocking Value from Unstructured Data, will explore how firms can harness the vast potential of unstructured data—emails, customer feedback, and other text-based information—to drive smarter decision-making and gain a competitive edge. Industry experts will share practical approaches to extracting insights, improving operational efficiency, and uncovering new business opportunities. If you're looking to turn your organisation’s unstructured data into a valuable asset, this session is not to be missed.
Register now: A-Team Insight link
3. Webinar: Five Essential Tips for Successful AI Adoption - 15 April 2025
This webinar, focuses on the critical role of data quality in AI success. As businesses rush to integrate AI, experts will discuss why clean, structured, and well-governed data must be a top priority to avoid AI becoming a liability. The session will cover key topics such as data governance, security, privacy, ethical considerations, and how to maximise AI ROI. Attendees will gain executive-level strategies to ensure AI delivers meaningful business impact.
Register now: CIO Dive link
4. In-Person Event: AI Breakfast & Roundtable – From AI Proof of Concept to Scalable Enterprise Adoption – 23 April 2025
Leading Point is hosting an exclusive AI Breakfast & Roundtable, bringing together AI leaders from top financial institutions, including banks, insurance firms, and buy-side institutions. This intimate, high-level discussion will explore the challenges and opportunities in scaling AI beyond proof of concept to enterprise-wide adoption.
Key discussion points include overcoming implementation barriers, aligning AI initiatives with business objectives, and best practices for AI success in banking, insurance, and investment management. This event offers a unique opportunity to connect with industry peers and gain strategic insights on embedding AI as a core driver of business value.
Want to be a part of the conversation?
If you are an executive with AI responsibilities in business, risk & compliance, or data contact Rajen Madan or Thushan Kumaraswamy to get a seat at the table.
Conclusion
The stories this week underscore a critical truth: AI governance isn’t just about compliance—it’s about building trust. From OpenAI’s push for federal oversight to IKEA’s ethical framework, the focus is shifting from rapid adoption to responsible deployment. The warnings from Turing Award winners Barto and Sutton are a stark reminder: innovation without safeguards is a risk we can’t afford.
As AI’s influence grows, the challenge is clear—businesses and policymakers must act now to bridge governance gaps, prioritise transparency, and ensure AI serves society as much as it drives progress. The future of AI depends on the choices we make today.
We’ll be back in two weeks with more insights. Until then, let’s keep pushing for a future where AI works for everyone.
The Trusted AI Bulletin #4
03/03/2025Regulation,Strategy,Investment Banking,Wealth Management,Data & Analytics,Artificial Intelligence,Newsletter,Risk,Data,Governance,PeopleInsurance
Issue #4 – Regulating AI: Balancing Innovation, Risk, and Global Influence
Introduction
Welcome to this edition of The Trusted AI Bulletin, where we explore the latest shifts in AI governance, regulation, and adoption. This week, we examine the UK’s evolving AI policy, the growing tensions between Big Tech and European regulators, and the strategic choices shaping AI’s future. With governments reassessing their regulatory approaches and businesses navigating complex compliance landscapes, the conversation around responsible AI is more urgent than ever.
AI adoption requires firms to focus on key capabilities, baseline their AI maturity, and articulate AI risks more effectively. Discussions with executives highlight a gap between the C-suite and AI leads, making governance alignment a critical success factor.
Whether you’re a policymaker, business leader, or AI enthusiast, our curated insights will help you stay informed on the key trends shaping the future of AI.
Key Highlights of the Week
1. UK Postpones AI Regulation to Align with US Policies
The UK government has postponed its anticipated AI regulation bill, originally slated for release before Christmas, now expected in the summer. This delay aims to align the UK's AI policies with the deregulatory stance of President Trump's administration, which has recently dismantled previous AI safety measures. Ministers express concern that premature regulation could deter AI businesses from investing in the UK, especially as the US adopts a more laissez-faire approach.
This strategic shift underscores the UK's intent to remain competitive in the global AI landscape, particularly against the backdrop of the EU's stricter regulatory proposals. However, this move has sparked debate over the balance between fostering innovation and ensuring ethical AI development.
Our take: This as a critical moment for businesses to take a proactive approach to AI governance rather than waiting for regulatory clarity. Firms must self-regulate by adopting strong AI controls and risk frameworks to ensure ethical and responsible AI deployment.
Source: The Guardian link
2. Big Tech vs Brussels: Silicon Valley Ramps Up Fight Against EU AI Rules
Silicon Valley’s biggest players, led by Meta, are intensifying their efforts to weaken the EU’s stringent AI and digital market regulations—this time with backing from the Trump administration. Lobbyists see an opportunity to pressure Brussels into softening enforcement of the AI Act and Digital Markets Act, with Meta outright refusing to sign up to the EU’s upcoming AI code of practice. The European Commission insists it will uphold its rules, but its recent decision to drop the AI Liability Directive suggests some willingness to compromise. If European regulators waver, it could set a dangerous precedent, emboldening Big Tech to dictate the terms of global AI governance.
Source: FT link
3. AI Safety Institute Rebrands, Drops Bias Research
The UK government has rebranded its AI Safety Institute, now called the AI Security Institute, shifting its focus away from AI bias and free speech concerns. Instead, the institute will prioritise cybersecurity threats, fraud prevention, and other high-risk AI applications. This move aligns the UK's AI policy more closely with the U.S. and has sparked debate over whether deprioritising bias research could have unintended societal consequences.
Our take: Bias and fairness remain core AI governance challenges. Firms need to go beyond regulatory mandates and build internal frameworks that address bias and transparency, ensuring trust in AI applications.
Should AI regulation focus solely on security threats, or is ignoring bias a step backward in responsible AI governance?
Source: UK Gov link
Featured Articles
1. UK AI Regulation Must Balance Innovation and Responsibility
The UK government’s approach to AI regulation will play a crucial role in shaping economic growth. The challenge lies in ensuring AI is safe, fair, and reliable without imposing rigid constraints that could stifle innovation. A risk-based, principles-driven framework—similar to the EU’s AI Act—offers a way forward, allowing adaptability while maintaining accountability. The real test will be whether regulation fosters trust and responsible AI use or becomes an obstacle to progress. Governance should encourage businesses to integrate ethical AI practices, not just comply with rules.
Our take: Striking this balance will be key to ensuring AI drives long-term economic and technological advancement. Firms shouldn’t wait for regulatory clarity. Assessing AI risks, implementing governance frameworks, and ensuring transparency now will give organisations a competitive edge.
Source: The Times link
2. Addressing Data and Expertise Gaps in AI Integration
In the rapidly evolving landscape of artificial intelligence, organisations face significant hurdles in adoption, notably concerns about data accuracy and bias, with nearly half of respondents expressing such apprehensions. Additionally, 42% of enterprises report insufficient proprietary data to effectively customise AI models, underscoring the need for robust data strategies. A similar percentage highlights a lack of generative AI expertise, pointing to a critical skills gap that must be addressed.
Moreover, financial justification remains a challenge, as organisations struggle to quantify the return on investment for AI initiatives. These challenges are particularly pertinent in the context of AI governance and regulation, emphasising the necessity for comprehensive frameworks to ensure ethical and effective AI deployment.
Source: IBM link
3. Global AI Compliance Made Easy – A Must-Have Tracker for AI Governance
The Global AI Regulation Tracker developed by Raymond Sun, is a powerful, interactive tool that keeps you ahead of the curve on AI laws, policies, and regulatory updates worldwide. With a dynamic world map, in-depth country profiles, and a live AI newsfeed, it provides a one-stop resource for navigating the complex and evolving AI governance landscape. Updated regularly, it ensures you never miss a critical regulatory shift that could impact your business or compliance strategy. Stay informed, stay compliant, and turn AI regulation into a competitive advantage.
Source: Techie Ray link
4. Breaking Down Barriers: Strategies for Successful AI Adoption
Artificial intelligence holds immense promise for revolutionising business operations, yet a staggering 80% of AI initiatives fall short of expectations. This high failure rate often stems from challenges such as subpar data quality, organisational resistance, and a lack of robust leadership support.
To navigate these obstacles, companies must prioritise comprehensive data management, foster a culture open to change, and ensure active engagement from leadership. Moreover, aligning AI projects with clear business objectives and investing in employee training are pivotal steps towards realising AI's full potential. Without addressing these critical areas, organisations risk squandering resources and missing out on the transformative benefits AI offers.
Source: Forbes link
Industry Insights
Case Study: AXA's Ethical AI Integration: Boosting Efficiency and Trust in Insurance
AXA, a global insurance leader, has strategically integrated Artificial Intelligence (AI) into its operations to enhance efficiency and uphold ethical standards. By implementing a dedicated AI governance team comprising actuaries, data scientists, privacy specialists, and business experts, AXA ensures responsible AI adoption across its services. This team focuses on creating transparent AI models, safeguarding data privacy, and maintaining human oversight in AI-driven decisions.
A practical application of this strategy is evident in AXA UK's deployment of 13 software bots within their claims departments, which, over six months, saved approximately 18,000 personnel hours and yielded around £140,000 in productivity gains. This initiative not only streamlines repetitive tasks but also reinforces AXA's commitment to ethical AI practices, setting a benchmark for the insurance industry.
Key Outcomes of AI Governance at AXA:
* Operational Efficiency: The introduction of AI bots has significantly reduced manual processing time, enhancing overall productivity.
* Ethical AI Deployment: Establishing a robust governance framework ensures AI applications are transparent, fair, and aligned with societal responsibilities.
* Enhanced Customer Service: Automation of routine tasks allows employees to focus on more complex customer needs, improving service quality.
Sources: Cap Gemini link, AXA link
Upcoming Events
1. Webinar: Augmenting Private Equity Expertise With AI – 6 March 2035
This event aims to explore practical strategies for private equity firms to integrate artificial intelligence, enhancing expertise and uncovering new value sources. Discussions will focus on AI's role in competitive deal sourcing, transforming due diligence processes, and bolstering risk management. As AI continues to reshape the financial landscape, this webinar offers timely insights into aligning technology strategies with business objectives, ensuring AI-driven value creation throughout the investment lifecycle.
Register now: FT Live link
2. Webinar: CIOs, Set the Right AI Strategy in 2025 – 7 March 2025
In this upcoming webinar, Chief Information Officers will gain insights into formulating effective AI strategies that yield measurable outcomes. The session aims to equip CIOs with the tools to navigate the complexities of AI implementation, ensuring alignment with organisational goals and compliance with emerging AI regulations. As AI continues to reshape industries, understanding its governance and regulatory landscape becomes imperative for IT leaders.
Register now: Gartner link
3. In-Person Event: AI UK 2025 Alan Turing Institute – 17 – 18 March 2025
This in-person event brings together experts to explore the latest advancements in artificial intelligence, governance, and regulation. A key highlight of the event is the panel discussion, Advancing AI Governance Through Standards, taking place on 18 March 2025.
Led by The AI Standard Hub, the session will delve into recent developments in AI assurance, global standardisation efforts, and strategies for fostering inclusivity in AI governance. As AI regulations continue to evolve, this discussion offers valuable insights into building a robust AI assurance ecosystem and ensuring responsible AI deployment.
Register now: Turing Institute link
Conclusion
As AI governance takes centre stage, the challenge remains—how do we drive innovation while ensuring transparency, fairness, and accountability? This issue underscores the importance of strategic regulation, ethical AI adoption, and proactive leadership in shaping a future where AI works for businesses and society alike. AI governance is shifting, but businesses can’t afford to wait. AI risks require more effort to understand, firms need to baseline their AI capabilities, and governance gaps between leadership and AI teams must be bridged.
With AI’s influence growing across industries, the need for informed decision-making has never been greater. Whether it’s policymakers refining regulations or organisations refining their AI strategies, the key takeaway is clear: responsible AI isn’t just about compliance—it’s about long-term success.
We’ll be back in two weeks with more insights—until then, let’s continue shaping a responsible AI future together.
The Trusted AI Bulletin #3
14/02/2025Regulation,Strategy,Investment Banking,Wealth Management,Data & Analytics,Artificial Intelligence,Newsletter,Risk,Data,Governance,PeopleInsurance
Issue #3 – Global AI Crossroads: Ethics, Regulation, and Innovation
Introduction
Welcome to this week’s edition of The Trusted AI Bulletin, where we explore the latest developments, challenges, and opportunities in the rapidly evolving world of AI governance. From global ethical debates to regulatory updates and industry innovations, this week’s highlights underscore the critical importance of balancing innovation with responsibility.
As AI continues to transform industries and societies, the need for robust governance frameworks has never been more urgent. For many organisations, this means not just keeping pace with regulatory change but also taking practical steps—such as bringing key teams together to assess AI usage, ensuring leadership is informed on emerging risks, and building governance frameworks that can evolve alongside innovation.
Join us as we delve into key stories shaping the future of AI governance and examine how organisations and nations are navigating this complex landscape.
Key Highlights of the Week
1. UK and US Withhold Support for Global AI Ethics Pact
At the AI Action Summit in Paris, the UK and US refused to sign a joint declaration on ethical and transparent AI, which was backed by 61 countries, including China and EU nations. The UK cited concerns over a lack of "practical clarity" and insufficient focus on security, while the US objected to language around "inclusive and sustainable" AI. Both governments stressed the need for further discussions on AI governance that align with their national interests. Critics and AI experts warn that this decision is a missed opportunity for democratic nations to take the lead in shaping AI governance, potentially allowing other global powers to set the agenda.
Source: The Times link
2. New PRA Letter Outlines 2025 Expectations for UK Banks
The Prudential Regulation Authority (PRA) has issued a letter outlining its 2025 supervisory priorities for UK banks, focusing on risk management, governance, and resilience. With ongoing market volatility, AI adoption, and geopolitical uncertainty, firms are expected to strengthen their risk frameworks and controls.
Liquidity and funding will also be under scrutiny, as the Bank of England shifts to a new reserve management approach. Meanwhile, banks must demonstrate by March 2025 that they can maintain operations during severe disruptions.
Notably, the Basel 3.1 timeline has been pushed to 2027, giving firms more time to adjust. However, regulatory focus on AI, cyber risks, and data management is set to increase, with further updates expected later this year.
Source: PRA PDF link
3. G42 and Microsoft Launch the Middle East’s First Responsible AI Initiative
G42 and Microsoft have jointly established the Responsible AI Foundation, the first of its kind in the Middle East, aiming to promote ethical AI standards across the Middle East and Global South. Supported by the Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), the foundation will focus on advancing responsible AI research and developing governance frameworks that consider cultural diversity. Inception, a G42 company, will lead the programme, while Microsoft plans to expand its AI for Good Lab to Abu Dhabi. This initiative underscores a commitment to ensuring AI technologies are safe, fair, and aligned with societal values.
Source: G42.ai link
Featured Articles
1. AI is Advancing Fast—Why Isn’t Governance Keeping Up?
Artificial intelligence is evolving at breakneck speed, reshaping industries and daily life, yet a clear governance framework is still missing. Effective policies must be based on scientific reality, not speculation, to address real-world challenges without stifling progress. Striking the right balance between innovation and regulation is crucial, especially as AI’s impact grows. Open access to AI models is key to driving research and ensuring future breakthroughs aren’t limited to a select few. With AI set to transform everything from healthcare to energy, the question remains—can governance keep pace?
Source: FT link
2. The EU AI Act: What High-Risk AI Systems Must Get Right
The EU AI Act imposes stringent obligations on high-risk AI systems, requiring organisations to implement risk management frameworks, ensure data governance, and maintain transparency. CIOs and CDOs must oversee compliance, ensuring human oversight, proper documentation, and clear communication when AI is in use.
A key focus is ensuring AI systems are explainable and auditable, enabling regulators and stakeholders to understand how decisions are made. Non-compliance carries significant financial and operational risks, making early alignment with regulatory requirements essential.
With enforcement approaching, businesses must integrate these rules into their AI strategies to maintain trust, mitigate risks, and drive responsible innovation. To stay ahead, organisations should conduct internal audits, update governance policies, and invest in staff training to embed compliance across AI initiatives. Proactive action now will determine competitive advantage in an AI-regulated future.
Source: A&O Shearman link
3. Building a Data-Driven Culture: Four Essential Pillars for Success
While many organisations collect vast amounts of data, few truly unlock its transformative potential. Success lies in mastering four critical elements: leadership commitment to champion data use, fostering data literacy across teams, ensuring data is accessible and integrated, and establishing trust through robust governance. Without these pillars, even the most data-rich organisations risk inefficiency and missed opportunities. A strong data-driven culture isn’t just about tools—it’s about embedding these principles into the fabric of your organisation.
Source: MIT Sloan link
Industry Insights
Case Study: Ocado’s Approach to Responsible AI Governance
Ocado Group has embedded AI across its operations, from optimising warehouse logistics to enhancing customer experiences. However, as AI adoption scales, so do the risks—unintended biases, unpredictable decision-making, and regulatory challenges. To navigate this, Ocado has placed responsible AI governance at the heart of its strategy, ensuring its models remain transparent, fair, and reliable.
A key component of its AI governance Strategy is its Responsible AI Framework, built around five key principles: Fairness, Transparency, Governance, Robustness, and Impact. This structured approach ensures AI systems are rigorously tested to prevent bias, remain explainable, and function as intended across complex operations.
One tangible success of this framework is Ocado’s real-time AI-powered monitoring, which has led to £100,000 in annual cost savings by automatically detecting and resolving system anomalies. With AI observability tools tracking over 100 microservice applications within its Ocado Smart Platform (OSP), the company can proactively address inefficiencies, minimising downtime and enhancing system reliability.
AI governance ensures Ocado’s AI models remain resilient and accountable, reducing risks associated with unpredictable AI behaviour. By embedding responsible AI principles into its operations, Ocado continues to optimise efficiency, prevent costly errors, and align with evolving regulatory expectations around AI.
Sources: Ocado Group link, Ocado Group link (CDO interview)
Upcoming Events
1. In-Person Event: Microsoft AI Tour - 5 March 2025
The Microsoft AI Tour in London is an event for professionals looking to explore the transformative potential of artificial intelligence. Featuring expert-led sessions, interactive workshops, and live demonstrations, it offers a unique opportunity to dive into the latest AI innovations and their real-world applications. Whether you're looking to expand your knowledge, network with industry leaders, or discover how AI can drive impact, this event is an invaluable experience for anyone invested in the future of technology.
Register now: MS link
2. In-Person Event: IRM UK Data Governance Conference - 17-20 March
The Data Governance, AI Governance & Master Data Management Conference Europe is scheduled for 17–20 March 2025 in London. This four-day event offers five focused tracks, covering topics such as data quality, MDM strategies, and AI ethics. The conference features practical case studies from leading organisations, providing attendees with actionable insights into effective data management practices.
Key sessions include “Navigating the Intersection of Data Governance and AI Governance” and “How Master Data Management can Enable AI Adoption”. Participants will also have opportunities to connect with over 250 data professionals during dedicated networking sessions.
Register now: IRM UK link
3. Webinar: Strategies and solutions for unlocking value from unstructured data - 27 March 2025
Discover how to harness the untapped potential of unstructured data in this insightful webinar. The session will explore practical strategies and innovative solutions to extract actionable insights from data sources like emails, documents, and multimedia. Attendees will gain valuable knowledge on overcoming challenges in data management, leveraging advanced technologies, and driving business value from previously underutilised information.
Register now: A-Team Insight link
Conclusion
As we wrap up this edition of The Trusted AI Bulletin, it’s clear that the journey toward ethical and effective AI governance is both challenging and essential. From the UK and US withholding support for a global AI ethics pact to Ocado’s pioneering approach to responsible AI, the stories this week highlight the diverse perspectives and strategies shaping the future of AI.
While progress is being made, the road ahead demands collaboration, innovation, and a shared commitment to ensuring AI benefits all of humanity. For organisations looking to act now, investing in education, cross-functional AI collaboration, and a clear governance roadmap will be key to staying competitive in an AI-regulated future.
Stay tuned for more updates, and let’s continue working together to build a future where AI is not only powerful but also fair, transparent, and accountable.
The Trusted AI Bulletin #2
31/01/2025Regulation,Strategy,Investment Banking,Wealth Management,Data & Analytics,Artificial Intelligence,Newsletter,Risk,Data,Governance,PeopleInsurance
Issue #2 – AI Investment, Ethics & Compliance Trends
Introduction
Welcome to this edition of The Trusted AI Bulletin, where we bring you the latest developments in enterprise AI risk management, adoption, and ethical AI practices.
This week, we examine how tech giants are investing billions into AI innovation, the growing global alignment on AI safety, and why passive data management is no longer viable in an AI-driven world.
With AI becoming an integral part of finance, healthcare, and other critical industries, strong governance frameworks are essential to ensure trust, transparency, and long-term success.
From real-world case studies like DBS Bank’s AI journey to upcoming industry events, this issue gives you insights to help you stay ahead in the evolving AI landscape.
Key Highlights of the Week
1. Stargate: America's $500 Billion AI Power Play
The United States unveiled the Stargate Project, a $500 billion initiative over the next four years to establish the world's most extensive AI infrastructure and secure global dominance in the field. Led by OpenAI, Oracle, and SoftBank, with backing from President Trump, the project plans to build 20 massive data centres across the U.S., starting with a 1 million-square-foot facility in Texas. Beyond advancing AI capabilities, Stargate is also a strategic move to attract global investment capital, potentially limiting China’s access to AI funding. With its $500 billion commitment far surpassing China’s $186 billion AI infrastructure spending to date, the U.S. is making a bold play to corner the market and maintain its technological edge.
Source: Forbes link
2. China’s AI Firms Align with Global Safety Commitments, Signalling Convergence in Governance
Chinese AI companies, including DeepSeek, are rapidly advancing in the global AI race, with their latest models rivalling top Western counterparts. In a notable shift, 17 Chinese firms have signed AI safety commitments similar to those adopted by Western companies, signalling a growing alignment on governance principles. This convergence highlights the potential for international collaboration on AI safety, despite ongoing geopolitical competition. As AI development accelerates, upcoming forums like the Paris AI Action Summit may play a crucial role in shaping global AI governance.
Source: Carnegie Endowment research link
3. Lloyds Banking Group Expands AI Leadership with New Head of Responsible AI
Lloyds Banking Group has appointed Magdalena Lis as its new Head of Responsible AI, reinforcing its commitment to ethical AI development. With over 15 years of experience, including advisory roles for the UK Government and leadership at Toyota Connected Europe, Lis will focus on ensuring AI safeguards while advancing innovation. This move follows the appointment of Dr. Rohit Dhawan as Director of AI and Advanced Analytics in 2024, as Lloyds continues to grow its AI Centre of Excellence, now comprising over 200 specialists. As AI reshapes banking, Lloyds aims to balance technological advancement with responsible implementation.
Source: FF News link
Featured Articles
1. Why Passive Data Management No Longer Works in the AI Era
The days of passive data management are over—AI-driven organisations need a proactive approach to governance. Chief Data Officers (CDOs) must ensure that data is high-quality, well-structured, and compliant to fully unlock AI’s potential. This means implementing automation, real-time monitoring, and stronger governance frameworks to mitigate risks while enhancing decision-making. Without these measures, businesses risk falling behind in an increasingly AI-powered world. The article explores how CDOs can take control of their data strategy to drive innovation and maintain regulatory compliance.
Image source: Image generated using ImageFX
Source: Medium link
2. How Governance & Privacy Can Safeguard AI Development
As AI adoption accelerates, so do concerns over data exposure, compliance failures, and reputational damage. Informatica warns that without strong governance and privacy policies, organisations risk losing control over sensitive information. Proactive data management, human oversight, and clear accountability are crucial to ensuring AI is both powerful and responsible. Businesses must not only understand the data fuelling their AI models but also implement safeguards to prevent unintended consequences. In an AI-driven world, those who neglect governance may find themselves facing serious risks.
Source: A-Team Insight link
3. AI Literacy: The Key to Staying Ahead in an AI-Driven World
AI is transforming industries, but do your teams truly understand how to use it responsibly? Without proper AI literacy, businesses risk compliance failures, biased decision-making, and missed opportunities. A well-designed AI training programme helps employees navigate regulations, mitigate risks, and unlock AI’s full potential. From assessing knowledge gaps to tailoring content for different roles, the right approach ensures AI is used strategically and ethically. As AI continues to evolve, organisations that prioritise education will be better equipped to adapt and thrive.
Source: IAPP link
Industry Insights
Case Study: DBS Bank - AI Success Rooted in Robust Governance Framework
Harvard Business School’s recent case study on DBS Bank highlights the critical role of AI governance in executing a successful AI strategy. Headquartered in Singapore, DBS embarked on a multi-year digital transformation under CEO Piyush Gupta in 2014, incorporating AI to enhance business value and customer experience. As AI adoption scaled, DBS developed its P-U-R-E framework—emphasising Purposeful, Unsurprising, Respectful, and Explainable AI—to ensure ethical and responsible AI deployment. This governance-first approach has been instrumental in managing risks while maximising AI’s potential across banking operations.
In 2022, DBS began exploring Generative AI (Gen AI) use cases, adapting its governance frameworks to balance innovation with emerging risks. By leveraging its existing AI capabilities, the bank continues to integrate AI responsibly while maintaining regulatory compliance and trust.
Key Outcomes of AI Governance at DBS:
o Economic Impact: DBS anticipates its AI initiatives will generate over £595 million in economic benefits by 2025, following consecutive years of doubling impact.
o Enhanced Customer Experience: AI-driven hyper-personalised prompts assist customers in making better investment and financial planning decisions.
o Employee Development: AI supports employees with tailored career and upskilling roadmaps, fostering long-term career growth.
Sources: DBS Bank news link
Upcoming Events
1. Webinar: Transforming Banking with GenAI – 13 February 2025
Join the Financial Times for a webinar exploring the transformative potential of Generative AI (GenAI) in the banking sector. Industry leaders will discuss the latest GenAI applications, including synthetic data and self-supervised learning, and provide strategies for navigating the rapidly evolving AI landscape. Key topics include revolutionising core banking operations, building robust data strategies, and reskilling workforces for future challenges.
Register now: FT link
2. Webinar: AI Maturity & Roadmap: Accelerate Your Journey to AI Excellence – 27 February 2025
Gartner is hosting a webinar focusing on assessing AI maturity and exploring the transformative potential of AI within organisations. The session will utilise Gartner's AI maturity assessment and roadmap tools to outline key practices across seven workstreams essential for achieving AI success at scale. Attendees will gain insights into managing and prioritising activities to harness AI's full potential.
Register now: Gartner link
3. Webinar: What Do CIOs Really Care About? – 13 March 2025
Join IDC for an insightful webinar exploring the evolving priorities of Chief Information Officers in the digital era. The session will delve into how CIOs are balancing innovation with pragmatism, transitioning from traditional IT management to strategic leadership roles that drive business transformation. Attendees will gain perspectives on aligning technology initiatives with organisational goals and the critical role of CIOs in today's rapidly changing technological landscape.
Register now: IDC link
Conclusion
Implementing Trusted AI isn’t just a regulatory requirement—it’s a business imperative. As organisations integrate AI into critical decision-making, ensuring trust, transparency, and compliance will define long-term success. By staying informed on evolving policies, adopting strong governance frameworks, and fostering ethical AI practices, businesses can harness AI’s full potential while managing risks.
We’d love to hear your thoughts! Join the conversation, share your perspectives, and stay engaged with us as we navigate the future of responsible AI together.
See you in the next issue!
Rajen Madan
Thushan Kumaraswamy
The Trusted AI Bulletin #1
20/01/2025Regulation,Strategy,Investment Banking,Wealth Management,Data & Analytics,Artificial Intelligence,Newsletter,Risk,Data,Governance,PeopleInsurance
Issue #1 – AI Advancements and Regulatory Shifts
Introduction
Welcome to the inaugural edition of The Trusted AI Bulletin! As artificial intelligence continues to reshape industries, the importance of robust risk management, deployment processes, transparency and ethical oversight on AI cannot be overstated.
At Leading Point our mission is to help those responsible for implementing AI in enterprises deliver trusted, rapid AI innovations while removing the blockers – be it uncertainty around AI value, lack of trust with AI outputs or user adoption.
This newsletter is your bi-weekly guide to staying informed, inspired, and ahead of the curve in navigating the challenges with AI deployment and realise the opportunity of AI in your enterprise.
Key Highlights of the Week
1. AI Innovations in Financial Services
The UK’s AI sector continues to grow, attracting £200 million in daily private investment since July 2024, with notable contributions like CoreWeave’s £1.75 billion data centre investment. These advancements underscore the transformative potential of AI in sectors such as financial services. From cutting-edge AI models to emerging data infrastructure, staying ahead of these innovations is essential for leaders navigating this rapidly evolving space.
Source: UK Government link
2. UK AI Action Plan
The UK government has officially approved a sweeping AI action plan aimed at establishing a robust economic and regulatory framework for artificial intelligence. The plan focuses on ensuring AI is developed safely and responsibly, with a strong emphasis on promoting innovation while addressing potential risks. Key priorities include creating clear guidelines for AI governance, fostering collaboration between government and industry, and ensuring the UK remains a global leader in AI development. This action plan marks a significant step towards creating a balanced approach to AI regulation.
Source: Artificial Intelligence News link
3. Tech Nation to launch London AI Hub
Brent Hoberman’s Founder’s Forum, announced the London AI Hub in collaboration with European AI group Merantix, Onfido and Quench.ai founder Husayn Kassai and flexible office provider Techspace. The initiative aims to bring together a fragmented sector. Hoberman said the hub would act as a “physical nucleus for meaningful collaboration across founders, investors, academics, policymakers and innovators.
Source: UK Tech News link
Featured Articles
1. 10 AI Strategy Questions Every CIO Must Answer
Artificial intelligence is transforming industries, and CIOs play a key role in aligning AI initiatives with business objectives. The article outlines 10 critical questions that every CIO must answer to ensure successful AI strategy, from building governance frameworks to implementing ethical AI.
Source: CIO.com link
2. AI Regulations, Governance, and Ethics for 2025
The global landscape for AI regulation is evolving rapidly, with regions adopting diverse approaches to governance and ethics. In the UK, a traditionally light-touch, pro-innovation approach is now shifting toward proportionate legislation focused on managing risks from advanced AI models. With upcoming proposals and the UK AI Safety Institute’s pivotal role in global risk research, the country aims to balance innovation with safety.
Source: Dentons link
Industry Insights
Case Study: Mastercard
Mastercard’s commitment to ethical AI governance acts as a core part of its innovation strategy. Recognising the potential risks of AI, Mastercard developed a comprehensive framework to ensure its AI systems align with corporate values, societal expectations, and regulatory standards. This approach highlights the growing importance of AI governance in fostering trust, minimising risks, and enabling responsible innovation.
Key elements of Mastercard’s AI governance strategy include:
o Transparency and accountability: Regular audits and cross-functional oversight ensure AI systems operate fairly and responsibly.
o Ethical principles in practice: AI systems are designed to uphold fairness, privacy, and security, balancing innovation with societal and corporate responsibilities.
This case underscores how robust AI governance can help organisations navigate the complexities of AI deployment while maintaining trust and ethical integrity.
Source: IMD link
Upcoming Events
1. Webinar: A CISO Guide to AI and Privacy – 21 January 2025
Explore how to develop effective AI policies aligned with industry best practices and emerging regulations in this insightful webinar. Maryam Meseha and Janelle Hsia will discuss ethical AI use, stakeholder collaboration, and balancing business risks with opportunities. Learn how AI can enhance cybersecurity and drive innovation while maintaining compliance and trust.
Register now: Brighttalk link
2. The Data Advantage – Smarter Investments in Private Markets – 28 January 2025
This event, run by Leading Point, focuses on the transformative role of data and technology in private markets, bringing together investors, data professionals, and market leaders to explore smarter investment strategies. Key discussions will cover leveraging data-driven insights, integrating advanced analytics, and enhancing decision-making processes to maximise returns in private markets.
Register now: Eventbrite link
3. The Data Management Summit 2025 – 20 March 2025
The Data Management Summit London is a premier event bringing together data leaders, regulators, and technology innovators to discuss the latest trends and challenges in data management, particularly in financial services. Key topics include data governance, ESG data, cloud strategies, and leveraging AI and advanced analytics to drive innovation while maintaining regulatory compliance. It’s an excellent opportunity to network and learn from industry leaders.
Register now: A-Team Insight link
Conclusion
As AI continues to transform industries, the need for operating level clarity and adoption in AI becomes ever more pressing. By staying informed about the latest advancements, regulatory changes, and best practices in AI implementations, enterprises can navigate this landscape effectively and responsibly. We encourage you to engage with this content, share your insights, and join the conversation in our upcoming events and discussions.
Stay informed, stay responsible!
Rajen Madan
Thushan Kumaraswamy
Unlocking the Private Markets Opportunity with Data Enablement
05/12/2024ArticleRegulation,Strategy,Wealth Management,Data & Analytics,Risk,Data,Governance,People
What is happening in Private Markets
As 2024 concludes, private capital markets have rebounded strongly, marking a year of recovery and strategic innovation following the challenges of 2023. Stabilising macroeconomic conditions, moderating inflation, and moderating interest rates have fuelled renewed M&A activity. Dry powder remains at historic highs of $3.9 trillion globally, yet Limited Partners (LPs) are increasingly pressing General Partners (GPs) to accelerate capital deployment and deliver returns from legacy investments. This momentum has pushed global private capital assets under management (AUM) past $12 trillion, underscoring sustained investor interest in asset classes such as private equity, venture capital, real estate, and infrastructure to create alpha.
Source: https://pitchbook.com/newsletter/toward-20-trillion-in-private-capital-aum
The democratisation of private markets is transforming the investment landscape. Regulatory changes and digital platforms are broadening access, enabling high-net-worth individuals, family offices, and retail investors to engage in opportunities once reserved for institutions. Recent examples of private market dynamism include BlackRock's acquisition of Global Infrastructure Partners, creating one of the largest infrastructure investment platforms globally.
Value creation is becoming a top priority for GPs with a focus on operating levers (good old working capital, digital, op model, costs and M&A) than historic approaches of financial engineering. With limited IPOs and traditional deal structure, the growth in secondaries markets is projected to be robust over the next three years. GPs need to play an ever more direct role in portfolio management and measurement to deliver the required returns.
Gen AI adoption in private markets is a real opportunity to create efficiency and deliver insights through the investment lifecycle. AI can enable firms to harness deeper insights and predictive modelling to identify opportunities, improve due diligence and risk assessment and formulate value creation strategies. ML algorithms can help automate valuations, enhance ESG compliance and provide enhanced portfolio oversight. Unlike the incremental evolution seen in Open Banking or FinTech, AI is promising a transformative impact.
The focus on market intelligence datasets, data platforms and AI solutions which enable LPs and GPs to harness the growth in private market asset classes, distribute to a much broader investor base including retail and leverage data and AI at scale has reached a critical mass.
BlackRock (NYSE: BLK) and Partners Group (SIX: PGHN) have teamed up to launch a multi-private markets models solution set to transform how retail investors access alternative investments. The solution will provide access to private equity, private credit and real assets in a single portfolio – currently not available to the U.S. wealth market - managed by BlackRock and Partners Group. Sep 12 2024
BlackRock has agreed to acquire Preqin, a UK-based independent provider of private markets data for £2.55bn ($3.2bn) in cash, combining Preqin’s data and research tools with Aladdin’s workflow functions into a single platform.
Abu Dhabi sovereign investor Mubadala Investment Company will participate in a $25 billion private credit, direct lending programme announced by Citigroup and alternative asset manager Apollo. Sep 27 2024
J.P. Morgan launched its Private Markets Data Solutions offering for institutional investors, available through Fusion by J.P. Morgan. This is a data management solution for private assets that enables investors, both General Partners (GP) and Limited Partners (LP), to analyse and gain transparency into their complete portfolio across public and private holdings and eliminate the manual processes of managing this operational workflow at scale.
Despite all this growth and promise there are significant impediments to Private Markets truly achieving the scale and opportunity which it promises.
What is the barrier to scale in Private Markets?
Lack of trusted centralised datasets and industry standard approaches
Unlike public market data, which is generally structured and standardised, private market data is incomplete, deemed proprietary, and inconsistently applied across participants in the value chain. In most instances, the absence of centralised data management frameworks means transaction granularity is often lacking, making it challenging to accurately analyse deal terms, valuations, and performance.
Data collection is fragmented, with limited transparency on capital flows, pricing dynamics, or asset-level specifics. Each institution produces information on their own basis, time periods and criteria. This is further compounded by diverse reporting standards, varying compliance requirements, and the manual processes prevalent in private market transactions.
Complexity in asset classes
Complexity in asset classes arises from the need to model diverse assets consistently across both public and private markets. Each asset class often has unique characteristics, valuation methods, and performance metrics, complicating standardised modelling. Furthermore, the integration of public and private data is essential to provide a holistic view of portfolios but presents significant challenges due to differences in data quality, reporting standards, and granularity.
Legacy investment management process & discipline
Complexity in asset classes arises from the need to model diverse assets consistently across both public and private markets. The end-to-end lifecycle from fund-raising, capital deployment, portfolio monitoring, portfolio administration and value creation ranges from ad hoc to sophisticated at many firms. This is partly due to the lack of trusted data and process challenges above but equally due to the investment discipline and focus across GPs and LPs.
Many GPs still think it is acceptable to provide historic low quality information, standard NAV statements, IMA summaries which don’t allow any sort of detailed attribution, forecasting, reporting and risk transparency that investors want and deserve; the detailed costs, exposures, mandates, fees, ESG tracking, transactions and activities of the underlying funds, portfolios, and transactions.
If GPs provide higher quality data, this will benefit both GPs and LPs – LPs in being able to be proactive to monitor investments, manage risk, take decisions on the basis of target returns and adjust allocations, and GPs to get closer to the value creation agenda and realise the investment opportunities.
The day-to-day consequences and risks to the system
These barriers have direct impacts:
⚫ Reducing the GPs ability to report performance to LPs, eroding trust and investor confidence
⚫ Lack of data integrity and a robust performance management process leads to inaccurate performance reporting, delays and errors in portfolio analysis, resulting in sub-optimal investment or financing decisions
⚫ Investment operations being labour-intensive and focused mostly on data extraction, and transformation with manual approaches create a challenge to produce frequent and detailed regulatory reporting
⚫ Operating model complexity in participants with fragmented and inefficient workflows and delivery structures, which are exacerbated by heightened M&A activity. The unit cost of servicing every additional $1Bn AUM and new integration required is not sustainable
Addressing these challenges requires an ecosystem-wide shift towards more cohesive data practices, leveraging technology to standardise inputs and improve accessibility whilst balancing the need to protect proprietary information and competitive advantage. Let us see how.
Leading Point’s Perspective: How can GPs and LPs take clear steps to build the foundational data layer and processes
We believe firms need to invest in creating capabilities and practices in five main areas.
Step | Objective | Actions |
1. Data standardisation and mastering | Achieve consistency, accuracy, and reliability of data across supply chain participants. | – Implement a mechanism to collect and aggregate data from managers, funds, administrators, and portfolio companies - a universal identifier and robust data dictionary is a pre-requisite
– Create capability to extract data from diverse formats, de-duplicate, validate across sources, and standardise into a unified structure. |
2. Business roles and rules for collaboration | Streamline workflows and enhance collaboration among supply chain participants. | – Define roles across your supply chain
– Define business rules and agreements you'd like to enforce including permissioned access and level of detail sharing – Automate processes for data handling, validation, and reporting – Establish and monitor performance benchmarks |
3. Technical stack | Technology to support growth, manage data complexity, and ensure high performance. | – APIs for seamless data integration and accessibility
– Store standardised data in scalable, high-performance databases – Maintain a full audit trail for data provenance and source traceability – Compute framework for valuation and exposures based on underlying transactions, funds, portfolios and prices incorporating various contracts, agreements and terms based on the counterparties |
4. Integrated platforms and ecosystem collaboration | Enable seamless interaction among participants through a shared, integrated infrastructure. | – Develop a data ecosystem for mutual benefit among participants
– Integrate with service providers and third parties |
5. Analysis, selection, and management of investments | Utilise high-quality data to inform investment decisions and optimise portfolio performance. | – Optimise portfolios using advanced analytics to identify opportunities and manage risks
– Integrate decision-support tools and models based on deep-data to forecast drawdowns, returns and attribution |
Firms need to actively consider their op model and be willing to entrust service providers who offer one or more of these five capabilities rather than attempt to build in-house. A robust data-enablement framework to manage and orchestrate the key inputs, outputs and oversight the firm needs is a pre-requisite before any outsourced service agreements. (See Leading Point's Data Enablement Framework below).
In the section after, we highlight examples (non-exhaustive) ranging from innovative Fintech platforms to global scale infrastructure providers.
The Leading Point Data Enablement Framework
We create the foundation for an enterprise to harness its data assets and make them integral to its business ops. Data becomes readily accessible, well-managed, and is used to drive decision-making and innovation.
Example Solutions in the Private Markets Space
Clearwater Analytics
Clearwater Analytics provides compelling evidence for LPs and GPs to adopt robust data foundations and solutions through its cloud-based platform for investment accounting, reporting, and analytics. The solution consolidates disparate financial data into a single source of truth, enabling real-time visibility across asset classes and geographies.
Key benefits include significant productivity gains, with 91% of data auto-reconciled using AI and machine learning, leading to reduced month- and quarter-end closing times. The platform's daily updated data and multi-currency reporting capabilities drive performance improvements and support expansion into new markets. Cost reductions are achieved through lower IT expenses and elimination of on-premises hardware.
JP Morgan Fusion Service
Fusion by J.P. Morgan offers a solution for institutional investors seeking a comprehensive view of their total portfolio across both public and private markets. This innovative platform addresses the longstanding challenge of fragmented and non-standardised private market data, which has historically limited investors' ability to analyse across asset classes effectively.
By leveraging advanced AI/ML technology and proprietary algorithms, Fusion seamlessly integrates and normalises data from diverse sources, including J.P. Morgan Securities Services, multiple portfolio administrators, and leading data providers. This integration spans a wide range of asset classes, from public securities to private equity, venture capital, real estate, and infrastructure.
Allvue Systems
Allvue Systems provides a comprehensive software solution tailored for alternative investment managers in private equity, venture capital, and private debt. Their integrated platform streamlines operations across front, middle, and back-office functions, encompassing portfolio management, compliance, data management, fund accounting, and financial reporting.
For LPs, Allvue centralises fund and portfolio information, significantly reducing manual processes and enhancing data management efficiency. The solution automates data collection and reporting, allowing LPs to self-serve their data needs through customisable reports. It also features user-defined dashboards and interactive reports that facilitate quick insights while supporting ESG tracking and reporting at both the portfolio company and fund levels. This capability enables the creation and collection of unlimited metrics for informed investment decisions.
Additionally, Allvue enhances investor relations with an Investor Portal that provides secure access to shared documents, fund data, and portfolio company information, streamlining communications between GPs and LPs.
Byhiras
Byhiras is a technology company dedicated to improving transparency and accountability in investment management. Its platform enables organisations, such as pension funds and asset managers, to aggregate and validate granular data about their investment activities. By providing detailed insights into costs and outcomes, Byhiras helps institutional investors make informed decisions, report accurately, and demonstrate value for money.
The platform benefits all stakeholders in the investment ecosystem. Investors gain clarity on how their funds are managed, consultants access data to evaluate value for money, and managers showcase their performance while maintaining confidentiality. Byhiras’ proprietary technology supports unlimited data types, while its tools ensure users retain full control over what data is shared and with whom.
Conclusion: Building a Data-Driven Future in Private Markets
As private markets navigate an era of unprecedented growth and complexity, the need for robust data transformation has never been greater. Addressing challenges such as fragmented data systems, non-standardised reporting, and evolving investor demands requires a strategic shift toward digitalisation and collaboration. Innovations in AI, cloud-based platforms, and integrated ecosystems are reshaping the industry, empowering General Partners and Limited Partners to make informed, data-driven decisions.
To thrive in this dynamic environment, market participants must embrace foundational changes—prioritising data standardisation, optimising operating models, and leveraging scalable technology solutions. Collaboration across the value chain will be critical in driving efficiency, transparency, and long-term value creation.
The future of private markets lies in their ability to adapt and harness the power of innovation. By addressing existing inefficiencies and adopting forward-looking strategies, the industry can secure its position as a cornerstone of global investment and sustainable growth.
Actionable Steps for Private Market Participants
⚫ Prioritise data enablement to unlock value across the investment lifecycle
⚫ Collaborate on standardisation efforts to reduce fragmentation
Join Us to Lead the Data Revolution!
Join Leading Point’s Private Markets data event on 28 January 2025 at Rise London in Shoreditch to explore transformative solutions with industry experts. Discover how a data-first approach improves transparency, decision-making, and risk management across the investment lifecycle.
Event Link: The Data Advantage - Smarter Investments in Private Markets
Sources
https://www.pwc.com/gx/en/services/deals/trends/2024/private-capital.html
Accelerating AI Success
16/10/2024EventInsurance,Regulation,Strategy,Investment Banking,Data & Analytics,Artificial Intelligence,Risk Management,Risk,Data,Governance,People
Accelerating AI Success: The Role of Data Enablement in Financial Services
Introduction
The webinar, held on 10 October 2024, focused on accelerating AI success and the foundational role of data enablement in financial services. Leading Point Founder & CEO, Rajen Madan, introduced the topic and the panel of four executives: Joanne Biggadike (Schroders), Nivedh Iyer (Danske Bank), Paul Barker (HSBC), and Meredith Gibson (Leading Point).
Rajen explained that data enablement involves "creating and harnessing data assets, making them super accessible and well managed, and embedding them into operational decision-making processes." He outlined the evolution of data management in the industry, describing three waves:
1️⃣ Focus on big warehouses and governance
2️⃣ Making data more pervasive and accessible
3️⃣ The opportunity now – emphasis on value extraction, embedding data insights in operational processes and decision-making and transform with AI
Data Governance and AI Governance
The panellists discussed the evolving role of data governance and its relationship to AI governance. Joanne Biggadike, Head of Data Governance at Schroders, noted the increasing importance of data governance: "Everybody's realising in order to move forward, especially with AI and generative AI, you really need your data to be reliable and you need to understand it."
She emphasised that while data governance and AI governance are separate, they are complementary. Biggadike stressed the importance of knowing data sources and having human oversight in AI processes: "We need a touch point. We need a human in the loop. We need to be able to review what we're coming out with as our outcomes, because we want to make sure that we're not coming out with the wrong output because the data's incorrect, or because the data's biased."
Paul Barker, Head of Data and Analytics Governance at HSBC cautioned against creating new silos for AI governance: "We've been doing model risk management for 30 years. We've been doing third party management for 30 years. We've been doing data governance for a very long time. So I think... it's about trying not to create a new silo.“
Data Quality and AI Adoption
Nivedh Iyer, Head of Data Management at Danske Bank, highlighted the importance of data quality in AI adoption: "AI in the space of data management, if I say core aspects of data management like governance, quality, lineage is still in the process of adoption... One of the main challenges for AI adoption is how comfortable we are... on the quality of the data we have because Gen AI or AI for that matter depends on good quality data."
Iyer also mentioned the emergence of innovative solutions in data quality management, particularly from fintech providers.
Central Shift and Technical Capabilities
Paul Barker emphasised the dual challenges of cultural shift and technical capabilities in data management: "There is a historic tendency to keep all the data secret... When you start with that as your DNA, it's then very difficult to move to a data democratisation culture where we're trying to surface data for the non-data professional."
Regarding technical capabilities, Barker noted the challenges faced by large, complex organisations compared to start-ups: "You can look at an organisation that's the scale and complexity of say HSBC... compared to a start-up organisation that literally starts its data architecture with a blank piece of paper and can build that Model Bank."
From a technical standpoint, large organisations face unique challenges in integrating various data sources across multiple markets and op models compared to smaller startups that can build their data architecture from scratch. There has been progress with technical solutions that can address some of these interoperability challenges.
Legal and Regulatory Aspects
Meredith Gibson, Data & Regulatory Lawyer with Leading Point, speaking from a legal perspective, highlighted the evolving regulatory landscape: "As the banks and other financial institutions... become more complex and more interested in data... so does the roadmap for how you control that change has morphed with deeper understanding by regulators and increased requirements."
She also raised concerns about data ownership in the context of AI and large language models: "Programmers have always done a copy and paste, which was fine until you end up with large language models where actually I'm not sure that people do know where their information and their data comes from."
The Panel highlighted the tension between banks' desire for autonomy in managing their data and regulators' need for standardisation to monitor activities effectively. There are several initiatives on standardisation including ISO, LEI and the EU AI Act. Lineage is crucial for getting AI ready. Who owns the data, who controls it, information on the data usage and obligations become central.
Leading Point’s Data Enablement Framework
Data is readily accessible, well-managed, and used to drive decision-making and innovation.
Data Strategy & Data Architecture
By having a clear data strategy and one that is aligned with the business strategy, you can reach better decisions quicker. Using insights from your data provides more confidence that the business actions you are taking are justified.
Having an agreed cross-business data architecture supports accelerated IT development and adoption of new products and solutions, by defining data standards, data quality, and data governance.
Data Catalogue & Data Virtualisation
Having a data catalogue is more than just implementing a tool like Collibra. It is important to define what that business data means at a logical level and how that is represented in the physical attributes.
A typical way to consolidate data is with a data warehouse, but that is a complex undertaking that requires migration from data sources into the warehouse with the associated additional storage costs. Data virtualisation simplifies data integration, standardisation, federation, and transformation without increasing data storage costs.
The Future of Data Enablement
The panellists discussed how data enablement needs to evolve to accommodate AI and other emerging technologies.
Joanne Biggadike suggested that while core principles of data governance remain useful, they need to adapt: "I think what they need to do is to make sure that they're not a blocker for AI, because AI is innovative and it actually means that sometimes you don't know everything that you might already need to know when you're doing day-to-day data governance."
Paul Barker noted the need for more dynamic governance processes: "We are now in the 21st century, but a lot of data governance is still based on a sort of 19th, early 20th century... form a committee, write a paper, have a six week period of consultation."
We need data governance by design. Financial institutions have been good with deploying SDLC, controlled and well-governed releases with checkpoints. We need to embed AI and data governance as part of the SDLC.
Data lineage, should not be a one-off solution it should be right-sized to the requirement i.e. coarse or fine-grained. Chasing detailed lineage across the complexity of large organisation infrastructures will take years and there will not be ROI. Pragmatism is required.
Focus on data ethics, as AI and ML becomes more widely-used, is as much a training and skills development requirement as a technical one. Understanding what terms and conditions underpin service, client conduct, usage of PII data and overall values of building customer trust.
Data ownership, rather than theoretical “who is to blame” when there are data quality issues, firms should focus on creating transparency on accountability and establishing clear chain of communications. Ownership can naturally align to domain data sets, for instance, CFO should have ownership on financial data. Central to ownership is establishing escalation points, “Who can I reach out to change something? Who is best placed to provide future integration?”
The climate impact of AI infrastructure is potentially significant, and firms need to factor this in their deployment. There will be innovation in data centres but also firms will get clarity of end state. Currently many organisations have gone through costly initiatives to move to cloud, and due to AI and security concerns firms are bringing some of it on-prem, this needs to be worked through.
We need to start thinking of AI as another tool that can accelerate and re-imagine processes making them more effective and efficient but it is not an innovation by itself and we should approach any AI adoption with what is the business problem we are looking to solve.
Challenges and Opportunities
The panellists identified several challenges and opportunities in the data and AI space:
1️⃣ Balancing innovation with governance and risk management
2️⃣ Ensuring data quality and reliability for AI applications
3️⃣ Adapting governance frameworks to be more agile and responsive
4️⃣ Addressing data ownership and privacy concerns in the age of AI
5️⃣ Bridging the gap between traditional data management practices and emerging technologies
Conclusion
The webinar highlighted the critical role of data enablement in accelerating AI success in financial services. The panellists stressed the need for robust data governance, high-quality data, and a cultural shift towards data democratisation. They also noted the importance of adapting existing governance frameworks to accommodate AI and other emerging technologies, rather than creating new silos.
As organisations continue to navigate the complex landscape of data and AI, they must balance innovation with risk management, ensure data quality and reliability, and address legal and ethical concerns. The future of data governance in financial services will likely involve more dynamic, agile processes that are embedded in business and operations and allow to keep pace with rapidly evolving technologies while maintaining the necessary controls and oversight. An overall pragmatic and principled approach is the best way forward for organisations.
Download the report
Leading Point - Webinar - Data Enablement for AI - Summary
Securing the GenAI Future
30/09/2024ArticleRegulation,Strategy,Change Leadership,Investment Banking,Data & Analytics,Artificial Intelligence,Risk Management,Risk,Data,Governance,People,Insurance
Securing the Future with GenAI Data Access Controls
Why you need data access controls for your GenAI systems
As generative AI (GenAI) rapidly transforms industries, the need for stringent data access controls is becoming a critical security priority. According to IBM's Cost of a Data Breach 2024 report1, a staggering 46% of breaches involve customer personal data, a concerning statistic as GenAI models increasingly process sensitive, proprietary, and personal data. With their ability to generate content and insights at scale, GenAI systems pose new challenges for securing data pipelines, making it essential for organisations to adopt granular, adaptive access controls to mitigate risks while harnessing the full potential of these powerful tools.
Objectives of GenAI access controls
To control access to a GenAI based application using the principle of least privilege, i.e., a user can only use appropriate prompts to interact with a GenAI application which in turn can only access data (approved for the user level) to provide inference (evaluated for appropriateness). This may involve any combination of control features like data classification & categorisation, role definition, role - resource mapping, attribute based permissioning, data masking, encryption at rest & transit.
Access control through roles and attributes
Role-based access control (RBAC) and the related attribute-based access control (ABAC) are methods of regulating access to computer, or a network resource based on the individual roles or other attributes of users within an organisation. RBAC ensures that only authorised individuals can access specific resources, performing only actions necessary for their roles. ABAC includes multiple attributes to determine access to resources (of which role can be one).
The benefit of RBAC is its simplicity; users do not need to manage or remember specific permissions as their role automatically determines their access. This facilitates changes in user roles and enhances security and compliance.
There are however challenges even at an organisational level in implementing a robust role-based access system. The challenges resulting from:
1️⃣ Role complexity with too many roles and a network role hierarchy
2️⃣ Role confusion where it is unclear which role is appropriate for a particular user or task
3️⃣ Maintaining role definition accuracy over time leading to outdated and inconsistent access controls
4️⃣ Managing joiner/mover/leaver (JML) processes and ensuring alignment with RBAC
5️⃣ Frequent changes in dynamic environments
Additionally, a more fine-grained access control will be necessary, as we delve into the complexities of an AI application deployment involving ever changing AI architectures. This is where extending RBAC and using attributes to develop an entitlement & permissioning system is an immediate necessity.
Specific challenges for implementing RBAC for GenAI
GenAI applications are those that use large language models (LLMs) to generate natural language texts or perform natural language understanding tasks.
LLMs are powerful tools that can enable various scenarios such as content creation, summarisation, translation, question answering, and conversational agents. However, LLMs also pose significant security challenges that need to be addressed by developers and administrators of GenAI applications. These challenges include:
1️⃣ Protecting the confidentiality and integrity of the data used to train and query the LLMs
2️⃣ Ensuring the availability and reliability of the LLMs and their services
3️⃣ Preventing the misuse or abuse of the LLMs by malicious actors or unintended users
4️⃣ Monitoring and auditing the LLMs' outputs and behaviours for quality, accuracy, and compliance
5️⃣ Managing the ethical and social implications of the LLMs' outputs and impacts
An effective GenAI access control requires a deep understanding of AI system architecture and a precise identification and definition of target features and objects accessible by AI users. These target features must be governed individually with entitlements, ensuring users and system resources have access to, can operate on and deliver information that is considerate of the entitlements defined.
While RBAC is well understood in enterprise security, implementing it for GenAI systems can, however, be challenging:
1️⃣ Inherent missing access controls: GenAI applications inherently do have not integrated RBAC features which can lead to a host of data privacy and security issues
2️⃣ Unstructured input: Inputs to Gen AI applications are usually unstructured. Request (prompts) are usually in natural language unlike the highly structured API calls for applications where identity-based policies are easier to implement
3️⃣ Natural language output: Typical outcomes from a Gen Ai application is in natural text that can contain any kind of information, in response to the request (prompts). These outcomes may contain sensitive information which may be in form of code or unstructured text
4️⃣ Model's inherent structure: AI models are inherently complex and sometimes monolithic. Controlling access to specific part of the model is complex
5️⃣ Extensibility: Advanced techniques like soft prompts & fine-tuning can extend a model’s existing functionality and are candidates for control
Given the impracticality of deploying multiple models, each trained specifically for individual roles in the RBAC system, access should not be binary. It should also consider additional variables such as hyper-parameters, used to control model behaviour.
The requirement to share the same model with multiple users, with different access, requires a more holistic view of access controls, taking into consideration the inputs, outputs and in case of retrieval augmented generation (RAG)-based models AI agents.
Pre-requisites for a successful RBAC/ABAC implementation
A successful RBAC implementation for GenAI requires a foundation of key pre-requisites. Organisations must first define clear roles, ensuring each role has well-articulated permissions tailored to data sensitivity and usage within the GenAI framework. Comprehensive data classification is crucial, enabling more granular control over who accesses specific datasets.
Additionally, regular audits and monitoring processes should be established to prevent privilege creep and ensure compliance. Lastly, cross-departmental collaboration is vital, as security, IT, and AI teams must align on policies to effectively manage the unique risks posed by GenAI systems while maintaining operational efficiency.
Role definition | Agree on role definition and role hierarchy |
Access mapping | Mapping of roles & resources to data access entitlements |
Data classification | Classification and categorisation of data |
Data labelling | Labelling raw data based on privacy, security & sensitivity |
Data curation | Creation of training, validation datasets & embedding incorporating data classification and privacy elements |
IAM (Identity & Access Management) | Roles to be aligned to IAM and managed via JML processes |
RBAC | Resource role-based policies for controlling access |
ABAC | Resource attributes-based policies for controlling access |
Implementation approach
Implementing RBAC for AI applications (GenAI, RAG-based, AI models), requires the understanding of the AI architecture in play and is best approached following the layered approach closely following the AI architecture components. GenAI models can generate diverse content, including its language output, audio, images, and even videos.
Our focus initially will be on LLMs, Large Language Models that generate natural language output. This creates a scope boundary and provides a view of the threat surface are to be covered for the LLM application and the formulation of controls required to ensure data privacy and security.
Subsequent enhancements will investigate how the controls can be extended to cover the additional complexities of handling multimodal capabilities of a Gen AI applications.
Securing LLM applications using access controls can be achieved by a layered approach, where each layer is secured from unauthorised access along with core data security such as masking and encryption. The layers are:
Layer 1: End user layer access control
* This layer controls who can access the GenAI tools themselves. It involves defining user roles and permissions to determine which employees can interact with the GenAI applications/ agents
* The focus is on ensuring that only authorised users can use the GenAI tools, thereby preventing unauthorised access to the system itself
Layer 2: AI layer access control
* At this layer, access controls what data and functionality the GenAI model & agents can access based on the permissions of the user making the request
* The AI model respects the user's role and permissions when processing prompts and retrieving information, ensuring that sensitive data is accessed only by users with appropriate clearance
Layer 3: Data layer access control
* At this layer, access controls what data AI model can access based on the permissions of the user making the request
* The focus here is controlling access to data being used and produced by the model
Layer 4: Infrastructure layer access control
* At this layer, access controls who has access to the infrastructure where AI solution has been deployed
* The focus here is in providing secure access to the GenAI deployment infrastructure
To ensure consistent security throughout the AI lifecycle, RBAC policies should integrate model architecture information, training procedures, data, and logs with the access policies for inference use. Adopting a data-centric approach in designing RBAC policies allows organisations to implement granular policies while treating AI systems as a single entity throughout their life cycles. While Role-Based Access Control (RBAC) may set a foundational baseline for security within enterprise AI systems, it falls short in offering the nuanced granularity required for data access by Agents and this is where the implementation of Attribute based access controls comes into play.
Put together RBAC and ABAC should provide a level of security which consummate with a secure use of GenAI applications.
Solution architecture for GenAI data access controls
A layered AI solutions architecture is outlined here:
Layer 1: End user layer access control |
||
THREATS | CONTROL | |
End user application
A web application as a front end to delivering inference |
· Access to GenAI application | · RBAC: controlling who has access to the web application |
AI Inputs
GenAI inputs or "Prompts" that instruct the GenAI model Prompt template |
· Prompt injection | · RBAC policy: to control access to templates
· Limiting prompt parameter length · Limiting to specific formats · Restricting parameter values to a predefined set |
Prompts intent detection | · Adversarial attack | · DLP: Security policies implemented at inference endpoints to ensure data privacy, sensitivity, exfiltration is controlled
· RBAC: Role based access to who (human), what (system) can access the API · Encrypted traffic: bidirectional encryption to control data exploitation while in transit |
API
To connect to the underlying application / model, transferring information bidirectionally |
· Access to GenAI application | · DLP: Security policies implemented at inference endpoints to ensure data privacy, sensitivity, exfiltration is controlled
· RBAC: Role based access to who (human), what (system) can access the API · Encrypted traffic: bidirectional encryption to control data exploitation while in transit |
AI outcomes
Outcomes from GenAI models (primarily unstructured) analysed and flagged for any potentially harmful or sensitive content with tagging |
· Harmful / Sensitive data leakage | · RBAC: Policies on harmful or sensitive content |
Layer 2: AI layer access control |
||
THREATS | CONTROL | |
LLM Model Weights
The trained critical parameters for the Model |
· Data / IP theft, model manipulation | · ABAC: Granular policies to control resources that can access / modify GenAI model weights
· Data Encryption: Ensure that all data used in training, both at rest and in transit, is encrypted. This protects against un-authorised access and tampering |
LLM Hyperparameters
Settings like temperature, input context size, and output size, influencing model behaviour and output |
· Model behaviour / IP theft | · ABAC: Granular policies to control resources that can access / modify GenAI model hyperparameters |
RAG LLM
To enable to access external data not included in the model during training |
· Data leakage, Data corruption, Service disruption | · ABAC: Granular policies by which users are assigned specific roles and permissions to use a particular agent / agent capability for agents, tools, and reader/retriever |
Training
Training GenAI models with RBAC features |
· Data poisoning | · Model Training: Ensuring that RBAC principles are applied during the data preprocessing, tokenisation and embedding stages. Models. This involves filtering the training data based on the defined access controls and ensuring that the LLM only learns from data appropriate for each user role.
· Adversarial training: A defensive algorithm that involves introducing adversarial examples into a model’s training data to teach the model to correctly classify these inputs as intentionally misleading. |
Vector DB
Used to store, index, and retrieve embeddings for use by the AI models |
· Data leakage, service disruption, model inference manipulation | · RBAC: Policies to ensure that only authorised personnel have access to the database, and even within that group, access levels are differentiated based on roles. |
Fine tuning
Models for fine-tuning LLM's (LORA) which introduce additional tuning parameters |
· Model behaviour, IP theft, data leakage | · ABAC: Granular policies to control resources that can access / modify GenAI model tuning parameters |
Layer 3: Data layer access control |
||
THREATS | CONTROL | |
Transactional & Analytical data store
Data-warehouse/ lakes: for Real time Transactional business data (Financial and non-financial) along Analytical & with slow changing data (HR data) |
· Data poisoning | · RBAC / ABAC: Granular control on data access within the organisation
· Data Encryption: Ensure that all data used in training, both at rest and in transit, is encrypted. This protects against un-authorised access and tampering. |
Journals data store
Settings like temperature, input context size, and output size, influencing model behaviour and output |
· Data & IP Theft, Service recovery disruption | · RBAC/ABAC: Granular control on data access within the organisation |
Layer 4: Infrastructure layer access control |
||
THREATS | CONTROL | |
Infrastructure
Development, Training & Production are some of the infrastructure environments used to deploy AI inferencing applications |
· Service disruption, Privacy & Compliance breach | · RBAC / ABAC: Granular control on AI infrastructure
· Data Encryption: Ensure that all data used in training, both at rest and in transit, is encrypted. This protects against un-authorised access and tampering |
Entitlements management Defining user entitlements and permissions required to enforce access controls | · Gaining unauthorised access to any system within the organisation/ Information theft | · RBAC: Policies to ensure that only authorised personnel have access to the entitlements management application and data. |
Conclusion
In conclusion, effectively implementing role-based access control (RBAC) for generative AI is crucial for safeguarding sensitive data while maximising the technology's potential. By establishing clear roles, conducting thorough data classification, and fostering collaboration across teams, organisations can create a robust security framework that mitigates risks associated with GenAI. Regular audits and monitoring will further enhance the system’s resilience against insider threats and compliance breaches. As the landscape of AI continues to evolve, organisations must remain proactive in refining their access control strategies to ensure that innovation does not come at the expense of security and data integrity.
References
WealthTech Tsunami
16/09/2024ArticleRegulation,Strategy,Wealth Management,Data & Analytics,Risk,Data,Governance,People,FinTech
WealthTech Tsunami: Will you ride the wave or wipe out?
Introduction
The wealth management industry is undergoing a significant transformation driven by the need to adapt to changing investor expectations. The tsunami of change from new regulations putting the customer first, adoption of artificial intelligence (AI), alternative data, to advanced analytics is reshaping traditional business models, emphasising the need for a more personalised, efficient, and adaptable approach. Let us dive into the ocean of WealthTech and see if you will ride the wave or wipe out.
Key Takeaways
1️⃣ AI automates wealth management processes, optimising investments, personalising strategies, and ensuring precision in financial operations. |
2️⃣ Embrace digital transformation in response to the rise of passive investing. Consider integrating robo-advisory services and adapt continuously to meet evolving client preferences and industry trends. |
3️⃣ Alternative data broadens insights for investment professionals, offering unique perspectives beyond traditional sources. Advanced analytics and machine learning enhance decision-making through meaningful information extraction. |
What is coming next?
In July 2023, the Financial Conduct Authority (FCA) introduced ‘Consumer Duty’ requiring banks, insurers, and wealth managers to provide a better standard of service to consumers. The FCA believes the regulatory benefits will only be felt if firms ensure they are “learning and improving continuously.”1 Financial institutions are advised to show evidence of this in their annual board report before 31 July 2024.
Consumer Duty is not a “once and done” exercise – warns FCA according to Financial Times
As 2024 has begun, the wealth management industry faces significant developments, including the rise of AI technology, a focus on sustainability, and the demand for personalised services. Regulations like the FCA's Consumer Duty are pushing firms towards a more customer-focused approach. In response, wealth managers must effectively balance technology with customer engagement to meet compliance requirements and adapt their businesses to the evolving customer demands.2
What is WealthTech?
We see WealthTech as three ways where technology can help the wealth management industry:
- New entrants using the latest innovations providing an alternative to traditional, and perhaps old-fashioned, wealth management firms
- New firms providing specific advanced technology solutions for incumbent wealth management firms to incorporate in their existing operations
- Firms offering new opportunities to expand traditional wealth management firms
WealthTech refers to the utilisation of technology, for example, big data and AI. This subdivision of FinTech aims to make wealth management and investment services more automated and efficient. There are various companies that are there to support the existing wealth management firms.
How will the “Great Wealth Transfer” transform the markets?
As trillions in assets flow to heirs over the next two decades, their investing preferences will create new opportunities. Some trends are already emerging, with increased desire for sustainable investing and an inherent mistrust of traditional “old school” wealth managers. According to Merrill, a Bank of America company, “ is set to change hands over the next 20 years, making it an opportune time to identify how using Wealth Tech can improve the customer experience.”3
We see these as drivers and opportunities from “Great Wealth Transfer” impact:
- Hyper-personalised service
- Growing adoption of digital experiences
- Deep understanding of the connected consumer
- A growing WealthTech ecosystem through financial data aggregators
- Expanded access for investors and advisors.
WealthTech companies disrupting traditional wealth management business models
Fundrise, Stash, or Toggle AI are typical WealthTech companies that are making a mark against traditional firms.
Fundrise helps investors invest in a portfolio of top-tier private technology companies before they IPO.
Stash makes investing easy for people by building on their own terms, making it stress-free, automated, with personalised & recurring investing advice.
Toggle AI is the user’s own financial analyst. Monitoring all market and fundamental data in real-time, it distils the observations into a cogent stream of timely investment insights. There is an element of “gamification” by allowing users to compare their predictions with actual market movements.
According to Deloitte, ‘The single most important disruptor in the industry today is the client. Investors and families have higher financial awareness, literacy, and access to information than at any other point in our history.’4
Ten disruptive trends in wealth management
1️⃣ The re-wired investor - Investors seek personalised and distinct advice, expecting tailored solutions aligned with their unique circumstances
2️⃣ Science vs human-based advice - The ongoing debate and integration of advanced analytics challenge traditional human-based advisory models
3️⃣ Analytics & big data - big data and analytics reshape decision-making, offering insights beyond traditional methods for more informed wealth management
4️⃣ Holistic, goals-based advice - shifting from product-centric approaches, wealth management embraces holistic, goals-based advice tailored to individual client aspirations
5️⃣ Democratisation of investment solutions - increasing accessibility and inclusivity in investment opportunities challenge traditional exclusivity in wealth management
6️⃣ Catching the retirement wave - Addressing the unique needs of an aging population and adapting services to cater to the retirement planning needs
7️⃣ Aging of advisors & upcoming transfer of wealth - A generational shift prompts the industry to prepare for the transfer of wealth and adapt to a changing advisory landscape.
8️⃣ New investment environment with three lows and two highs - low interest rates, low inflation rates, low rates of economic growth, high volatility, and high levels of financial leverage are redefining the investment landscape
9️⃣ Rising costs of risk and heavier regulatory burden - Increasing regulatory demands and risk management costs reshape operational strategies for wealth management firms
🔟 Convergence and new competitive patterns - Traditional boundaries blur as various financial services converge, introducing new competitive dynamics in the wealth management sector
Impact of the rewired investor on wealth management companies
The rewired investor views advice differently than prior generations and anticipates engaging with advisers in a new manner. Investors, for example, no longer wish to be addressed as a segment but as distinct people with distinct interests and preferences. Instead, they expect to receive advice tailored to their unique circumstances.
They also want to maintain control over their financial life, grasp the information they are given, and make critical decisions for themselves.
How is AI and machine learning influencing the wealth management industry?
Virtual assistants, fraud detection, algorithmic trading — these and more AI/ML use cases in finance can enrich your business. With these advanced technologies, you can benefit from enhanced security of the data, streamlined operations, reduced need for a workforce in repetitive tasks, informed decision-making, reduced human mistakes, and the resulting financial consequences and high customer satisfaction and loyalty.
To meet modern customer needs, wealth managers are succeeding with two key approaches:
1️⃣ Flat-Fee Advisory Models
- * Move away from product-focused models
- * Implement flat-fee advisory pricing based on client investment value
- * Enhance efficiency and productivity to maintain revenues
2️⃣ Personalised Services
- * Embrace needs-based personalisation
- * Equip relationship managers (RMs) for a range of solutions
- * Utilise advanced data and analytics for effective relationship management aligned with client life stages and goals
Conclusion
The "Great Wealth Transfer" and the emergence of the rewired investor underscore the importance of meeting evolving customer demands through tailored services and a client-centric focus. WealthTech companies are disrupting traditional models, and the industry must navigate disruptive trends, embrace digital transformation, and leverage AI and machine learning to stay competitive. The future of wealth management lies in a dynamic balance between technology and human-centric approaches, ensuring a seamless integration of innovation and client satisfaction. The key lies in riding the wave of change rather than risking wipe-out in the evolving landscape.
References
1 https://www.fca.org.uk/news/speeches/consumer-duty-not-once-and-done
2 https://kidbrooke.com/blog/gamification-and-simulation-tools-enhancing-the-wealth-management-customer-experience/
3 https://www.ml.com/articles/great-wealth-transfer-impact.html
4 https://www2.deloitte.com/content/dam/Deloitte/ch/Documents/financial-services/ch-deloitte-global-future-ready-investment-firm-long.pdf
Blueprints for Tomorrow
10/09/2024ArticleInsurance,Regulation,Strategy,Change Leadership,Investment Banking,Risk Management,Risk,Data,Governance,People,FinTech
The Evolution of Business Process Modelling in the AI Era
Introduction
In today’s rapidly evolving financial landscape, Business Process Management (BPM) has emerged as a critical driver of operational excellence and competitive advantage. Financial services firms are grappling with increasing regulatory complexities, heightened customer expectations, and the relentless pace of technological change. In this context, BPM offers a systematic approach to streamline processes, enhance efficiency, and foster innovation. By effectively managing and optimising business processes, financial institutions can not only improve their operational performance but also adapt swiftly to market dynamics, ensuring sustained growth and profitability.
Process Diagrams are NOT Process Models
Many firms make a limited effort to draw process diagrams in PowerPoint and Visio; PowerPoint, at least, is available for everyone to use in every firm. But the sheer volume of processes that need modelling overwhelms firms trying to do the right thing. There is often no agreed standard for capturing processes. BPMN (Business Process Model and Notation) is commonly used but can be arcane and too detailed. Flowchart formats are also used. But nothing is ever consistent.
A diagram is just a drawing; boxes, lines, and words on a page. A model takes those boxes, lines, and words and adds meaning, context, and relationships. A named activity in one process can be reused in another process. In a process diagram, this is just a copy with no link between the two. In a process model, the model knows that these two activities are the same thing.
Modelling takes a little more effort as you need to choose a modelling tool and standards and set up governance around your process models. But the small amount of time spent doing this will reap huge short and long-term benefits once you start modelling for real.
The Importance of BPM
According to a report by Gartner, organisations that implement BPM effectively can achieve a 20-30% improvement in process efficiency, highlighting its transformative potential in the financial sector. However, many institutions only scratch the surface by stopping at process modelling, missing out on the broader benefits of a fully optimised process landscape.
The benefits of process modelling include:
1️⃣ Transparency: By visualising processes, stakeholders gain a clear understanding of how various activities interconnect, facilitating better communication and collaboration across departments.
2️⃣ Optimisation: Process models help identify inefficiencies, redundancies, and bottlenecks, enabling organisations to implement targeted improvements that enhance performance and reduce costs.
3️⃣ Standardisation: Process modelling ensures consistency in operations, which is essential for maintaining quality and compliance in financial services.
4️⃣ Compliance: Detailed process documentation ensures that all activities adhere to regulatory standards and internal policies, reducing the risk of non-compliance and associated penalties.
5️⃣ Better Decision-Making: Comprehensive process analysis provides valuable insights that inform strategic planning and operational decisions, supporting data-driven management practices.
6️⃣ Training and Onboarding: Well-defined processes make it easier to train new employees and integrate them into the organisation. Some BPM tools enable you to create detailed procedures and manuals based on your existing processes.
7️⃣ Workflow: Once you have your processes modelled, you can use them to execute your processes in a workflow. A workflow controls how the process is executed by your teams. Workflows track metrics on process performance to provide opportunities for improvement based on actual results.
In our work with several financial institutions on operating models and process definition, we consistently observe a recurring challenge: many organisations miss out on realising the next significant wave of benefits from process optimisation. Their efforts often stall in debates over which vendor to select for process mapping and how to implement future processes. This approach limits their ability to unlock the full potential of process improvements. The integration of emerging technologies such as Artificial Intelligence (AI), low-code platforms, and process mining can be transformative, enabling institutions to break free from this cycle and drive more substantial, long-term value.
The Future of BPM - Unlocking Potential with Emerging Technologies
Artificial Intelligence (AI) is transforming BPM by introducing advanced capabilities for automation, optimisation, and decision-making. AI encompasses a range of technologies, including machine learning (ML), natural language processing (NLP), and robotic process automation (RPA), that enhance BPM systems' ability to manage and analyse complex, data-intensive tasks.
AI in BPM goes beyond simple automation to include intelligent process automation (IPA), which combines AI with traditional automation to handle more complex tasks. For instance, AI can analyse vast amounts of data to identify inefficiencies and patterns, predict outcomes, and generate actionable insights. This enables financial services firms to streamline operations, reduce manual intervention, and enhance decision-making processes. By integrating AI, organisations can achieve improved process efficiency, enhanced customer experiences, and better compliance.
Process Simulation
Process simulation involves creating virtual models of business processes to predict their performance under various scenarios. AI enhances process simulation by enabling more sophisticated modelling and forecasting. AI algorithms can simulate complex process interactions, predict potential bottlenecks, and evaluate the impact of different changes in real-time.
This proactive approach helps organisations anticipate challenges and make data-driven decisions before implementing changes. By leveraging AI for process simulation, financial services firms can test and refine processes in a controlled environment, leading to more effective and resilient operational strategies.
Process Mining
Process mining utilises data from event logs to visualise and analyse the actual flow of business processes. AI-powered process mining provides deeper insights by analysing large volumes of data to identify inefficiencies, compliance issues, and areas for improvement.
This technology allows organisations to uncover hidden patterns and deviations from standard procedures, leading to more informed and targeted process optimisations. With a projected compound annual growth rate of around 40% from 2022 to 2028, process mining will play a crucial role in enhancing process visibility and performance.
Intelligent Automation
Intelligent automation combines technologies such as robotic process automation (RPA), AI, machine learning (ML), and business process management (BPM) to enhance tasks and decision-making across organisations. By integrating AI with RPA, businesses can automate not only routine, repetitive tasks but also more complex processes such as fraud detection, customer onboarding, and regulatory compliance, all in real time. This reduces manual effort, minimises errors, and improves operational efficiency, enabling employees to focus on more strategic activities. As these technologies continue to evolve, intelligent automation will drive greater value, adaptability, and sustained growth for organisations.
Case Studies
Case Study 1: HSBC Enhances Customer Service with AI and BPM
A leading global bank, HSBC, integrated AI-powered chatbots into its customer service processes through a robust BPM framework. This integration resulted in a 40% reduction in response time and a 25% increase in customer satisfaction scores. HSBC also utilised process modelling to streamline its loan approval process, reducing approval times from weeks to days and improving overall operational efficiency. (Source: HSBC Annual Report 2021, Deloitte Insights on AI in Financial Services)
Case Study 2: Process Mining at Deutsche Bank
Deutsche Bank leveraged process mining technology to optimise its operational processes across various business units. The bank used Celonis, a leading process mining tool, to analyse millions of transaction records and uncover inefficiencies in their processes. By visualising and understanding their process flows in real-time, Deutsche Bank identified bottlenecks and deviations from standard procedures, leading to significant improvements in efficiency and compliance. For instance, they were able to reduce loan processing times by 15% and improve overall process conformance, resulting in enhanced customer satisfaction and reduced operational costs. (Source: Celonis Implementation at Deutsche Bank)
Case Study 3: Insurance Firm Streamlines Claims Processing with Low-Code Platforms
An international insurance company, Zurich Insurance Group, adopted a BPM solution to revamp its claims processing system. The implementation enabled rapid development and deployment of customised workflows, reducing processing times by 50% and decreasing operational costs by 30%. The enhanced process transparency and automation also led to improved compliance and audit readiness. (Source: Forrester Research on Low-Code Platforms, Zurich Insurance Case Study by Appian)
Actionable Steps for Realising the Opportunity with BPM
Financial services firms looking to adopt and enhance BPM should consider the following actionable steps:
1️⃣ Start Small: Don’t try to model everything. Pick one important process that you know is not working. Think about the process at a high-level to begin with. Once you have the basic process modelled, you can drill down into more detail. Then, you can expand to other high priority areas.
2️⃣ Work Top-Down: In the process you have chosen, what are the 5-7 most important activities that happen in the process? Once you have captured those in your model. Pick each one of those 5-7 and go into the next level of detail.
3️⃣ Define Clear Objectives and KPIs: Establish specific goals for BPM initiatives aligned with overall business strategy and identify key performance indicators to measure success.
4️⃣ Conduct a Comprehensive Process Audit: Begin by mapping and analysing existing processes to identify areas for improvement and prioritise initiatives based on impact and feasibility.
5️⃣ Leverage Appropriate Technologies: Select and implement technologies such as AI, low-code platforms, and cloud solutions that align with organisational needs and capabilities.
6️⃣ Seed with a Skilled Team: Invest in training and developing a team skilled in BPM methodologies and technologies with in-house and partners, fostering a culture of continuous improvement and innovation.
7️⃣ Adopt an Iterative Development Approach: Embrace rapid prototyping and iterative development to quickly deliver initial versions of new processes. Get these processes into use early, gathering feedback from real-world application, and then refine them based on this feedback. This approach accelerates time to value and ensures that solutions are continuously improved in response to actual user needs and evolving business conditions.
8️⃣ Monitor and Refine Continuously: Regularly review process performance against KPIs and make necessary adjustments to sustain and enhance improvements over time.
Conclusion
Business Process Management is not merely a tool for operational efficiency; it is a strategic enabler that empowers financial services firms to navigate complexity, embrace innovation, and achieve sustained competitive advantage. By focusing on the next wave of opportunity with BPM, financial institutions can optimise their processes, integrate emerging technologies, and adapt to the ever-changing market dynamics.
For those interested in delving deeper, we offer access to the results of our extensive analysis of 100 BPM solutions. Our evaluation covered key aspects such as capabilities, technical functionality, and product architecture.
To discuss these insights further or to understand how they can be applied to your organisation, please contact Leading Point co-founder and process specialist Thush, who is available to provide expert guidance and tailored recommendations.
References and Further Reading
1️⃣ "Business Process Management: Concepts, Languages, Architectures" by Mathias Weske
2️⃣ Gartner Research Reports on BPM and Emerging Technologies
3️⃣ "The Ultimate Guide to Business Process Management" by BPMInstitute.org
4️⃣ Deloitte’s Insights on "Transforming Financial Services through BPM"
5️⃣ "Process Mining: Data Science in Action" by Wil van der Aalst
AI Under Scrutiny
09/07/2024ArticleRegulation,Strategy,Change Leadership,Investment Banking,Data & Analytics,Artificial Intelligence,Risk Management,Risk,Data,Governance,People,Insurance
Why AI risk & governance should be a focus area for financial services firms
Introduction
As financial services firms increasingly integrate artificial intelligence (AI) into their operations, the imperative to focus on AI risk & governance becomes paramount. AI offers transformative potential, driving innovation, enhancing customer experiences, and streamlining operations. However, with this potential comes significant risks that can undermine the stability, integrity, and reputation of financial institutions. This article delves into the critical importance of AI risk & governance for financial services firms, providing a detailed exploration of the associated risks, regulatory landscape, and practical steps for effective implementation. Our goal is to persuade financial services firms to prioritise AI governance to safeguard their operations and ensure regulatory compliance.
The Growing Role of AI in Financial Services
AI adoption in the financial services industry is accelerating, driven by its ability to analyse vast amounts of data, automate complex processes, and provide actionable insights. Financial institutions leverage AI for various applications, including fraud detection, credit scoring, risk management, customer service, and algorithmic trading. According to a report by McKinsey & Company, AI could potentially generate up to $1 trillion of additional value annually for the global banking sector.
Applications of AI in Financial Services
1 Fraud Detection and Prevention: AI algorithms analyse transaction patterns to identify and prevent fraudulent activities, reducing losses and enhancing security.
2 Credit Scoring and Risk Assessment: AI models evaluate creditworthiness by analysing non-traditional data sources, improving accuracy and inclusivity in lending decisions.
3 Customer Service and Chatbots: AI-powered chatbots and virtual assistants provide 24/7 customer support, while machine learning algorithms offer personalised product recommendations.
4 Personalised Financial Planning: AI-driven platforms offer tailored financial advice and investment strategies based on individual customer profiles, goals, and preferences, enhancing client engagement and satisfaction.
Potential Benefits of AI
The benefits of AI in financial services are manifold, including increased efficiency, cost savings, enhanced decision-making, and improved customer satisfaction. AI-driven automation reduces manual workloads, enabling employees to focus on higher-value tasks. Additionally, AI's ability to uncover hidden patterns in data leads to more informed and timely decisions, driving competitive advantage.
The Importance of AI Governance
AI governance encompasses the frameworks, policies, and practices that ensure the ethical, transparent, and accountable use of AI technologies. It is crucial for managing AI risks and maintaining stakeholder trust. Without robust governance, financial services firms risk facing adverse outcomes such as biased decision-making, regulatory penalties, reputational damage, and operational disruptions.
Key Components of AI Governance
1 Ethical Guidelines: Establishing ethical principles to guide AI development and deployment, ensuring fairness, accountability, and transparency.
2 Risk Management: Implementing processes to identify, assess, and mitigate AI-related risks, including bias, security vulnerabilities, and operational failures.
3 Regulatory Compliance: Ensuring adherence to relevant laws and regulations governing AI usage, such as data protection and automated decision-making.
4 Transparency and Accountability: Promoting transparency in AI decision-making processes and holding individuals and teams accountable for AI outcomes.
Risks of Neglecting AI Governance
Neglecting AI governance can lead to several significant risks:
1 Embedded bias: AI algorithms can unintentionally perpetuate biases if trained on biased data or if developers inadvertently incorporate them. This can lead to unfair treatment of certain groups and potential violations of fair lending laws.
2 Explainability and complexity: AI models can be highly complex, making it challenging to understand how they arrive at decisions. This lack of explainability raises concerns about transparency, accountability, and regulatory compliance
3 Cybersecurity: Increased reliance on AI systems raises cybersecurity concerns, as hackers may exploit vulnerabilities in AI algorithms or systems to gain unauthorised access to sensitive financial data
4 Data privacy: AI systems rely on vast amounts of data, raising privacy concerns related to the collection, storage, and use of personal information
5 Robustness: AI systems may not perform optimally in certain situations and are susceptible to errors. Adversarial attacks can compromise their reliability and trustworthiness
6 Impact on financial stability: Widespread adoption of AI in the financial sector can have implications for financial stability, potentially amplifying market dynamics and leading to increased volatility or systemic risks
7 Underlying data risks: AI models are only as good as the data that supports them. Incorrect or biased data can lead to inaccurate outputs and decisions
8 Ethical considerations: The potential displacement of certain roles due to AI automation raises ethical concerns about societal implications and firms' responsibilities to their employees
9 Regulatory compliance: As AI becomes more integral to financial services, there is an increasing need for transparency and regulatory explainability in AI decisions to maintain compliance with evolving standards
10 Model risk: The complexity and evolving nature of AI technologies mean that their strengths and weaknesses are not yet fully understood, potentially leading to unforeseen pitfalls in the future
To address these risks, financial institutions need to implement robust risk management frameworks, enhance data governance, develop AI-ready infrastructure, increase transparency, and stay updated on evolving regulations specific to AI in financial services.
The consequences of inadequate AI governance can be severe. Financial institutions that fail to implement proper risk management and governance frameworks may face significant financial penalties, reputational damage, and regulatory scrutiny. The proposed EU AI Act, for instance, outlines fines of up to €30 million or 6% of global annual turnover for non-compliance. Beyond regulatory consequences, poor AI governance can lead to biased decision-making, privacy breaches, and erosion of customer trust, all of which can have long-lasting impacts on a firm's operations and market position.
Regulatory Requirements
The regulatory landscape for AI in financial services is evolving rapidly, with regulators worldwide introducing guidelines and standards to ensure the responsible use of AI. Compliance with these regulations is not only a legal obligation but also a critical component of building a sustainable and trustworthy AI strategy.
Key Regulatory Frameworks
1 General Data Protection Regulation (GDPR): The European Union's GDPR imposes strict requirements on data processing and the use of automated decision-making systems, ensuring transparency and accountability.
2 Financial Conduct Authority (FCA): The FCA in the UK has issued guidance on AI and machine learning, emphasising the need for transparency, accountability, and risk management in AI applications.
3 Federal Reserve: The Federal Reserve in the US has provided supervisory guidance on model risk management, highlighting the importance of robust governance and oversight for AI models.
4 Monetary Authority of Singapore (MAS): MAS has introduced guidelines for the ethical use of AI and data analytics in financial services, promoting fairness, ethics, accountability, and transparency (FEAT).
5 EU AI Act: This new act aims to protect fundamental rights, democracy, the rule of law and environmental sustainability from high-risk AI, while boosting innovation and establishing Europe as a leader in the field. The regulation establishes obligations for AI based on its potential risks and level of impact.
Importance of Compliance
Compliance with regulatory requirements is essential for several reasons:
1 Legal Obligation: Financial services firms must adhere to laws and regulations governing AI usage to avoid legal penalties and fines.
2 Reputational Risk: Non-compliance can damage a firm's reputation, eroding trust with customers, investors, and regulators.
3 Operational Efficiency: Regulatory compliance ensures that AI systems are designed and operated according to best practices, enhancing efficiency and effectiveness.
4 Stakeholder Trust: Adhering to regulatory standards builds trust with stakeholders, demonstrating a commitment to responsible and ethical AI use.
Identifying AI Risks
AI technologies pose several specific risks to financial services firms that must be identified and mitigated through effective governance frameworks.
Bias and Discrimination
AI systems can reflect and reinforce biases present in training data, leading to discriminatory outcomes. For instance, biased credit scoring models may disadvantage certain demographic groups, resulting in unequal access to financial services. Addressing bias requires rigorous data governance practices, including diverse and representative training data, regular bias audits, and transparent decision-making processes.
Security Risks
AI systems are vulnerable to various security threats, including cyberattacks, data breaches, and adversarial manipulations. Cybercriminals can exploit vulnerabilities in AI models to manipulate outcomes or gain unauthorised access to sensitive financial data. Ensuring the security and integrity of AI systems involves implementing robust cybersecurity measures, regular security assessments, and incident response plans.
Operational Risks
AI-driven processes can fail or behave unpredictably under certain conditions, potentially disrupting critical financial services. For example, algorithmic trading systems can trigger market instability if not responsibly managed. Effective governance frameworks include comprehensive testing, continuous monitoring, and contingency planning to mitigate operational risks and ensure reliable AI performance.
Compliance Risks
Failure to adhere to regulatory requirements can result in significant fines, legal consequences, and reputational damage. AI systems must be designed and operated in compliance with relevant laws and regulations, such as data protection and automated decision-making guidelines. Regular compliance audits and updates to governance frameworks are essential to ensure ongoing regulatory adherence.
Benefits of Effective AI Governance
Implementing robust AI governance frameworks offers numerous benefits for financial services firms, enhancing risk management, trust, and operational efficiency.
Risk Mitigation
Effective AI governance helps identify, assess, and mitigate AI-related risks, reducing the likelihood of adverse outcomes. By implementing comprehensive risk management processes, firms can proactively address potential issues and ensure the safe and responsible use of AI technologies.
Enhanced Trust and Transparency
Transparent and accountable AI practices build trust with customers, regulators, and other stakeholders. Clear communication about AI decision-making processes, ethical guidelines, and risk management practices demonstrates a commitment to responsible AI use, fostering confidence and credibility.
Regulatory Compliance
Adhering to governance frameworks ensures compliance with current and future regulatory requirements, minimising legal and financial repercussions. Robust governance practices align AI development and deployment with regulatory standards, reducing the risk of non-compliance and associated penalties.
Operational Efficiency
Governance frameworks streamline the development and deployment of AI systems, promoting efficiency and consistency in AI-driven operations. Standardised processes, clear roles and responsibilities, and ongoing monitoring enhance the effectiveness and reliability of AI applications, driving operational excellence.
Case Studies
Several financial services firms have successfully implemented AI governance frameworks, demonstrating the tangible benefits of proactive risk management and responsible AI use.
JP Morgan Chase
JP Morgan Chase has established a comprehensive AI governance structure that includes an AI Ethics Board, regular audits, and robust risk assessment processes. The AI Ethics Board oversees the ethical implications of AI applications, ensuring alignment with the bank's values and regulatory requirements. Regular audits and risk assessments help identify and mitigate AI-related risks, enhancing the reliability and transparency of AI systems.
ING Group
ING Group has developed an AI governance framework that emphasises transparency, accountability, and ethical considerations. The framework includes guidelines for data usage, model validation, and ongoing monitoring, ensuring that AI applications align with the bank's values and regulatory requirements. By prioritising responsible AI use, ING has built trust with stakeholders and demonstrated a commitment to ethical and transparent AI practices.
HSBC
HSBC has implemented a robust AI governance framework that focuses on ethical AI development, risk management, and regulatory compliance. The bank's AI governance framework includes a dedicated AI Ethics Committee, comprehensive risk management processes, and regular compliance audits. These measures ensure that AI applications are developed and deployed responsibly, aligning with regulatory standards and ethical guidelines.
Practical Steps for Implementation
To develop and implement effective AI governance frameworks, financial services firms should consider the following actionable steps:
Establish a Governance Framework
Develop a comprehensive AI governance framework that includes policies, procedures, and roles and responsibilities for AI oversight. The framework should outline ethical guidelines, risk management processes, and compliance requirements, providing a clear roadmap for responsible AI use.
Create an AI Ethics Board
Form an AI Ethics Board or committee to oversee the ethical implications of AI applications and ensure alignment with organisational values and regulatory requirements. The board should include representatives from diverse departments, including legal, compliance, risk management, and technology.
Implement Specific AI Risk Management Processes
Conduct regular risk assessments to identify and mitigate AI-related risks. Implement robust monitoring and auditing processes to ensure ongoing compliance and performance. Risk management processes should include bias audits, security assessments, and contingency planning to address potential operational failures.
Ensure Data Quality and Integrity
Establish data governance practices to ensure the quality, accuracy, and integrity of data used in AI systems. Address potential biases in data collection and processing, and implement measures to maintain data security and privacy. Regular data audits and validation processes are essential to ensure reliable and unbiased AI outcomes.
Invest in Training and Awareness
Provide training and resources for employees to understand AI technologies, governance practices, and their roles in ensuring ethical and responsible AI use. Ongoing education and awareness programs help build a culture of responsible AI use, promoting adherence to governance frameworks and ethical guidelines.
Engage with Regulators and Industry Bodies
Stay informed about regulatory developments and industry best practices. Engage with regulators and industry bodies to contribute to the development of AI governance standards and ensure alignment with evolving regulatory requirements. Active participation in industry forums and collaborations helps stay ahead of regulatory changes and promotes responsible AI use.
Conclusion
As financial services firms continue to embrace AI, the importance of robust AI risk & governance frameworks cannot be overstated. By proactively addressing the risks associated with AI and implementing effective governance practices, firms can unlock the full potential of AI technologies while safeguarding their operations, maintaining regulatory compliance, and building trust with stakeholders. Prioritising AI risk & governance is not just a regulatory requirement but a strategic imperative for the sustainable and ethical use of AI in financial services.
References and Further Reading
- McKinsey & Company. (2020). The AI Bank of the Future: Can Banks Meet the AI Challenge?
- European Union. (2018). General Data Protection Regulation (GDPR).
- Financial Conduct Authority (FCA). (2019). Guidance on the Use of AI and Machine Learning in Financial Services.
- Federal Reserve. (2020). Supervisory Guidance on Model Risk Management.
- JP Morgan Chase. (2021). AI Ethics and Governance Framework.
- ING Group. (2021). Responsible AI: Our Approach to AI Governance.
- Monetary Authority of Singapore (MAS). (2019). FEAT Principles for the Use of AI and Data Analytics in Financial Services.
For further reading on AI governance and risk management in financial services, consider the following resources:
- "Artificial Intelligence: A Guide for Financial Services Firms" by Deloitte
- "Managing AI Risk in Financial Services" by PwC
- "AI Ethics and Governance: A Global Perspective" by the World Economic Forum
Strengthening Information Security
17/04/2024ArticleRegulation,Strategy,Investment Banking,Corporate Banking,Data & Analytics,Risk Management,Data Quality,Data Governance,Risk,Data,Governance,People,Insurance
The Combined Power of Identity & Access Management and Data Access Controls
The digital age presents a double-edged sword for businesses. While technology advancements offer exciting capabilities in cloud, data analytics, and customer experience, they also introduce new security challenges. Data breaches are a constant threat, costing businesses an average of $4.45 million per incident according to a 2023 IBM report (https://www.ibm.com/reports/data-breach) and eroding consumer trust. Traditional security measures often fall short, leaving vulnerabilities for attackers to exploit. These attackers, targeting poorly managed identities and weak data protection, aim to disrupt operations, steal sensitive information, or even hold companies hostage. The impact extends beyond the business itself, damaging customers, stakeholders, and the broader financial market
In response to these evolving threats, the European Commission (EU) has implemented the Digital Operational Resilience Act (DORA) (Regulation (EU) 2022/2554). This regulation focuses on strengthening information and communications technology (ICT) resilience standards in the financial services sector. While designed for the EU, DORA’s requirements offer valuable insights for businesses globally, especially those with operations in the EU or the UK. DORA mandates that financial institutions define, approve, oversee, and be accountable for implementing a robust risk-management framework. This is where identity & access management (IAM) and data access controls (DAC).
The Threat Landscape and Importance of Data Security
Data breaches are just one piece of the security puzzle. Malicious entities also employ malware, phishing attacks, and even exploit human error to gain unauthorised access to sensitive data. Regulatory compliance further emphasises the importance of data security. Frameworks like GDPR and HIPAA mandate robust data protection measures. Failure to comply can result in hefty fines and reputational damage.
Organisations, in a rapidly-evolving hybrid working environment, urgently need to implement or review their information security strategy. This includes solutions that not only reduce the attack surface but also improve control over who accesses what data within the organisation. IAM and DAC, along with fine-grained access provisioning for various data formats, are critical components of a strong cybersecurity strategy.
Keep reading to learn the key differences between IAM and DAC, and how they work in tandem to create a strong security posture.
Identity & Access Management (IAM)
Think of IAM as the gatekeeper to your digital environment. It ensures only authorised users can access specific systems and resources. Here is a breakdown of its core components:
- Identity Management (authentication): This involves creating, managing, and authenticating user identities. IAM systems manage user provisioning (granting access), authentication (verifying user identity through methods like passwords or multi-factor authentication [MFA]), and authorisation (determining user permissions). Common identity management practices include:
- Single Sign-On (SSO): Users can access multiple applications with a single login, improving convenience and security.
- Multi-Factor Authentication (MFA):An extra layer of security requiring an additional verification factor beyond a password (e.g., fingerprint, security code).
- Passwordless: A recent usability improvement removes the use of passwords and replaces them with authentication apps and biometrics.
- Adaptive or Risk-based Authentication: Uses AI and machine learning to analyse user behaviour and adjust authentication requirements in real-time based on risk level.
- Access Management (authorisation): Once a user has had their identity authenticated, then access management checks to see what resources the user has access to. IAM systems apply tailored access policies based on user identities and other attributes. Once verified, IAM controls access to applications, data, and other resources.
Advanced IAM concepts like Privileged Access Management (PAM) focus on securing access for privileged users with high-level permissions, while Identity Governance ensures user access is reviewed and updated regularly.
Data Access Control (DAC)
While IAM focuses on user identities and overall system access, DAC takes a more granular approach, regulating access to specific data stored within those systems. Here are some common DAC models:
- Discretionary Access Control (also DAC): Allows data owners to manage access permissions for other users. While offering flexibility, it can lead to inconsistencies and security risks if not managed properly. One example of this is UNIX files, where an owner of a file can grant or deny other users access.
- Mandatory Access Control (MAC): Here, the system enforces access based on pre-defined security labels assigned to data and users. This offers stricter control but requires careful configuration.
- Role-Based Access Control (RBAC): This approach complements IAM RBAC by defining access permissions for specific data sets based on user roles.
- Attribute-Based Access Control (ABAC): Permissions are granted based on a combination of user attributes, data attributes, and environmental attributes, offering a more dynamic and contextual approach.
- Encryption: Data is rendered unreadable without the appropriate decryption key, adding another layer of protection.
IAM vs. DAC: Key Differences and Working Together
While IAM and DAC serve distinct purposes, they work in harmony to create a comprehensive security posture. Here is a table summarising the key differences:
FEATURE
IAM
DAC
Description
Controls access to applications
Controls access to data within applications
Granularity
Broader – manages access to entire systems
More fine-grained – controls access to specific data check user attributes
Enforcement
User-based (IAM) or system-based (MAC)
System-based enforcement (MAC) or user-based (DAC)
Imagine an employee accessing customer data in a CRM system. IAM verifies their identity and grants access to the CRM application. However, DAC determines what specific customer data they can view or modify based on their role (e.g., a sales representative might have access to contact information but not financial details).
Dispelling Common Myths
Several misconceptions surround IAM and DAC. Here is why they are not entirely accurate:
- Myth 1: IAM is all I need. The most common mistake that organisations make is to conflate IAM and DAC, or worse, assume that if they have IAM, that includes DAC. Here is a hint. It does not.
- Myth 2: IAM is only needed by large enterprises. Businesses of all sizes must use IAM to secure access to their applications and ensure compliance. Scalable IAM solutions are readily available.
- Myth 3: More IAM tools equal better security. A layered approach is crucial. Implementing too many overlapping IAM tools can create complexity and management overhead. Focus on choosing the right tools that complement each other and address specific security needs.
- Myth 4: Data access control is enough for complete security. While DAC plays a vital role, it is only one piece of the puzzle. Strong IAM practices ensure authorised users are accessing systems, while DAC manages their access to specific data within those systems. A comprehensive security strategy requires both.
Tools for Effective IAM and DAC
There are various IAM and DAC solutions available, and the best choice depends on your specific needs. While Active Directory remains a popular IAM solution for Windows-based environments, it may not be ideal for complex IT infrastructures or organisations managing vast numbers of users and data access needs.
Imagine a scenario where your application has 1,000 users and holds sensitive & personal customer information for 1,000,000 customers split across ten countries and five products. Not every user should see every customer record. It might be limited to the country the user works in and the specific product they support. This is the “Principle of Least Privilege.” Applying this principle is critical to demonstrating you have appropriate data access controls.
To control access to this data, you would need to create tens of thousands of AD groups for every combination of country or countries and product or products. This is unsustainable and makes choosing AD groups to manage data access control an extremely poor choice.
The complexity of managing nested AD groups and potential integration challenges with non-Windows systems highlight the importance of carefully evaluating your specific needs when choosing IAM tools. Consider exploring cloud-based IAM platforms or Identity Governance and Administration (IGA) solutions for centralised management and streamlined access control.
Building a Strong Security Strategy
The EU’s Digital Operational Resilience Act (DORA) emphasises strong IAM practices for financial institutions and will coming into act from 17 January 2025. DORA requires financial organisations to define, approve, oversee, and be accountable for implementing robust IAM and data access controls as part of their risk management framework.
Here are some key areas where IAM and DAC can help organisations comply with DORA and protect themselves:
DORA Pillar
How IAM helps
How DAC helps
ICT risk management
- Identifies risks associated with unauthorised access/misuse
- Detects users with excessive permissions or dormant accounts
- Minimises damage from breaches by restricting access to specific data
ICT related incident reporting
- Provides audit logs for investigating breaches (user activity, login attempts, accessed resources)
- Helps identify source of attack and compromised accounts
- Helps determine scope of breach and potentially affected information
ICT third-party risk management
- Manages access for third-party vendors/partners
- Grants temporary access with limited permissions, reducing attack surface
- Restricts access for third-party vendors by limiting ability to view/modify sensitive data
Information sharing
- Permissions designated users authorised to share sensitive information
- Controls access to shared information via roles and rules
Digital operational resilience testing
- Enables testing of IAM controls to identify vulnerabilities
- Penetration testing simulates attacks to assess effectiveness of IAM controls
- Ensures data access restrictions are properly enforced and minimizes breach impact
Understanding IAM and DAC empowers you to build a robust data security strategy
Use these strategies to leverage the benefits of IAM and DAC combined:
- Recognise the difference between IAM and DAC, and how they are implemented in your organisation
- Conduct regular IAM and DAC audits to identify and address vulnerabilities
- Implement best practices like the Principle of Least Privilege (granting users only the minimum access required for their job function)
- Regularly review and update user access permissions
- Educate employees on security best practices (e.g., password hygiene, phishing awareness)
Explore different IAM and DAC solutions based on your specific organisational needs and security posture. Remember, a layered approach that combines IAM, DAC, and other security measures like encryption creates the most effective defence against data breaches and unauthorised access.
Conclusion
By leveraging the combined power of IAM and DAC, you can ensure only the right people have access to the right data at the right time. This fosters trust with stakeholders, protects your reputation, and safeguards your valuable information assets.
Helping a leading insurance provider improve their data access controls
02/04/2024Regulation,Strategy,Data & Analytics,Case Study,Risk,Data,Governance,PeopleInsurance
A global insurance provider had begun migrating their legacy on-premise applications to a new data lake. With a strategic reporting solution used, it was clear that report users had access to data that they did not need to have access to.
Previous studies had identified the gaps and it was time to push forward and deliver a solution. We were engaged to define the roles and data access control business rules to support Germany, as they had specific requirements around employee name visibility. A temporary solution had been implemented but a strategic solution that unmasked employee names to those who needed to see them, was required.
We developed the rules with support from the Claims business, the Data Protection Officer, and German Works Council. We designed and built a Power BI prototype to demonstrate the rules working using attribute-based access controls (ABAC).
This prototype and the business rules have led to a further engagement to implement the solution in a real report connected to the data lake.
Top 5 Trends for MLROs in 2024
28/03/2024ArticleRegulation,Strategy,Investment Banking,Corporate Banking,Risk Management,Risk,Data,Governance,People,Insurance
Our Financial Crime Practice Lead, Kavita Harwani, recently attended the FRC Leadership Convention at the Celtic Manor, Newport, Wales. This gave us the opportunity to engage with senior leaders in the financial risk and compliance space on the latest best practices, upcoming technology advances, and practical insights.
Criminals are becoming increasingly sophisticated, driving MLROs to innovate their financial crime controls. There is never a quiet time for FRC professionals, but 2024 is proving to be exceptionally busy.
Our view on the top five trends that MLROs need to focus on is presented here.
Top 5 Trends
- Minimise costs by using technology to scan the regulatory horizon and identify impacts on your business
- Accelerating transaction monitoring & decisioning by applying AI & data analytics
- Optimising due diligence with a 360 view of the customers
- Improving operational efficiency by using machine learning to automate alert handling
- Reducing financial crime risk through training and communications programmes.
1. Regulatory Compliance and Adaptation
MLROs need to stay abreast of evolving regulatory frameworks and compliance requirements. With regulatory changes occurring frequently, MLROs must ensure their organisations are compliant with the latest anti-money laundering (AML) and counter-terrorist financing (CTF) regulations.
This involves scanning the regulatory horizon, updating policies, procedures, and systems to reflect regulatory updates and adapting swiftly to new compliance challenges.
2. Technology & Data Analytics
MLROs will increasingly leverage advanced technology and data analytics tools to enhance their AML capabilities.
Machine learning algorithms and predictive analytics can help identify suspicious activities more effectively, allowing MLROs to detect and prevent money laundering and financial crime quicker, at lower cost, and with higher accuracy rates.
MLROs must focus on implementing robust AML technologies and optimising data analytics strategies to improve risk detection and decision-making processes.
3. Customer Due Diligence (CDD) and Enhanced Due Diligence (EDD)
MLROs should prioritise strengthening CDD processes to better understand their customers’ risk of committing financial crimes.
Enhanced due diligence is critical for high-risk customers, such as politically exposed persons (PEPs) and high net worth individuals (HNWIs).
MLROs should focus on enhancing risk-based approaches to CDD and EDD, leveraging technology and data analytics to streamline customer onboarding processes while maintaining compliance with regulatory requirements.
4. Transaction Monitoring and Suspicious Activity Reporting
MLROs will continue to refine transaction monitoring systems to effectively identify suspicious activities and generate accurate alerts for investigation.
MLROs should focus on optimising transaction monitoring rules and scenarios to reduce false positives and prioritise high-risk transactions for further review.
Enhanced collaboration with law enforcement agencies and financial intelligence units will be crucial for timely and accurate suspicious activity reporting. Cross-industry collaboration is an expanding route to quicker insights on bad actors and behaviours.
5. Training and Awareness Programmes
MLROs must invest in comprehensive training and awareness programs to educate employees on AML risks, obligations, and best practices.
Building a strong culture of compliance within the organisation is essential for effective AML risk management.
Additionally, MLROs must promote a proactive approach to AML compliance, encouraging employees to raise concerns and seek guidance when faced with potential AML risks.
Conclusion
The expanded use of technology and data is becoming more evident from our discussions. The latest, ever-accelerating, improvements in automation and AI has brought a new set of opportunities to transform legacy manual, people-heavy processes into streamlined, efficient, and effective anti-financial crime departments.
Leading Point has a specialist financial crime team and can help strengthen your operations and meet these challenges in 2024. Reach out to our practice lead Kavita Harwani on kavita@leadingpoint.io to discuss your needs further.
Improving data access controls at a global insurer
12/03/2024TestimonialsFinTech,Insurance,Regulation,Strategy,Change Leadership,Data & Analytics,Risk Management,Risk,Data,Governance,People
"We approached Leading Point to support the enhancement of strategic data lake fine grained access controls capabilities. Their partnership approach working transversally across business and IT functions quickly surfaced root causes to be addressed as part of the improvement plan. Leading Point's approach to consulting services was particularly refreshing from a quality and cost stand point compared to some of the traditional players that we had consulted with before."
Head of Data Controls at Global Corporate Insurer
Helping a US broker-dealer manage its application estate using open source tools
11/03/2024Strategy,Wealth Management,Data & Analytics,Risk Management,Case Study,Risk,Data,Governance,PeopleRegulation
Our client was a Fortune 500 US independent broker-dealer with over 17,500 financial advisors and over 1tn USD in advisory and brokerage assets. They had a large application estate with nearly 1,000 applications they had either developed, bought or acquired through mergers and takeovers. The applications were captured in ServiceNow CMDB but there was little knowledge around flows, owners, data, and batch jobs.
Additionally, the client also wanted to roll out a new data strategy. Part of this engagement with their business community was to educate and inform about the data strategy and its impact on their work.
We were asked to implement an open source enterprise architecture tool called Waltz. Waltz had been originally developed at Deutsche Bank and had recently been released as open source software under FINOS (Fintech Open Source Foundation). Waltz is not widely-known in financial services yet and we saw this as a great opportunity to demonstrate the benefits of using open source tools.
To support the data strategy rollout, the client asked if we could build a simple and clear internal website to show the new data strategy and data model. The data model would be navigable to drill-down into more detail and provide links to existing documentation.
Our approach:
With our extensive implementation experience, we put together a small, experienced, cross-border team to deploy and configure Waltz. We knew that understanding the client's data was key; what data was required, where was it, how good was its quality. Waltz uses data around:
- Organisational units - different structures depending on the viewpoint (business, technical)
- People - managerial hierarchies, roles, responsibilities
- Applications - owners, technologies, costs, licences, flows, batch jobs
- Data - hierarchies, entities, attributes, definitions, quality, owners, lineage
- Capabilities - owners, services, processes
- Change - initiatives, costs, impact
We split our work into a number of workstreams:
- Data readiness - understand what data they had, the sources, and the quality
- Data configuration - understand the relationships between the data and prepare it for Waltz
- Waltz implementation - understand the base open source version of Waltz with its limitations, gather the client requirements (like single-sign on and configurable data loaders), develop the features into Waltz, and deploy Waltz at the client
- Data strategy website - understand the audience, design website prototype options for client review, build an interactive React website for the rollout roadshows
The project was challenging because, as ever, the state of the data. There were multiple inconsistencies which hinders the use of tooling to bring order. We needed to identify those inconsistencies, see who should own them, and ensure they were resolved.
With the flexibility of an enterprise architecture tool, it was important to be clear around the specific problems we wanted to solve for the client. We identified 10+ potential use cases that we worked with the client to narrow down. Future extensions of the project enabled us to extend into these other use cases.
One such problem was around batch job documentation. The client had thousands of Word docs specifying batch jobs transferring data between internal and external applications. These documents were held in SharePoint, Confluence, and local drives. This made it difficult to find information about specific batch jobs if something went wrong, for example.
We used the applications captured in Waltz and linked them together. We developed a new data loader that could import Word docs and extract the batch job information automatically from them. This was used to populate Waltz and make this information searchable, reducing the time spent by Support teams to find out about failed jobs.
One common negative that is raised about similar applications is the effort involved to get data into the application. Waltz accelerates this by sending surveys out to crowd-source knowledge from across the organisation. We found this a great way of engaging with users and capturing their experience into Waltz.
Our results:
We were able to deploy an open source enterprise architecture tool on a client's AWS cloud within three months. This included adding new features, such as single sign-on, improving existing Waltz capabilities, like the data loaders, and defining the data standards to enable smooth data integrations with source systems.
Using Waltz showed the client the value of bringing together disparate knowledge from around the organisation into one place. It does expose data gaps, but we always see this as a benefit for the client, as any improvement in data quality yields improved business results.
Helping a UK retail bank to benchmark their ESG progress against their peers
26/02/2024Strategy,Corporate Banking,Data & Analytics,Risk Management,Case Study,Risk,Data,Governance,PeopleRegulation
Our client wanted to improve their ESG position against their competitors, based on real data. They were unsure about where to start with ESG measurement and integrating ESG philosophy into their culture and business processes.
We were asked to come up with an ESG scoring model that could use existing public data from the client's peers against their own internal reporting data. This scoring model would be used to place the client against their peers in environmental, social, and governance groups, as well as an overall rating. Our ESG expertise was recognised in identifying which ESG frameworks could support this scoring model. We were also tasked with ensuring that their ESG philosophy was aligned to their purpose.
Our approach:
We used an example of best-in-class ESG stewardship in a Tier 1 financial services firm as a demonstration of what is possible. This case study covered how ESG impacted the firm across:
- Partnerships
- Products & services
- Diversity & inclusion
- Climate change
- Governance & ESG frameworks
We created an ESG scoring model that used existing ESG frameworks, such as SASB and UN SDGs. This scoring model included 32 questions across E, S and G categories. We researched public company reports to find data and references to key ESG themes. Thresholds were used to classify metrics and create a weighted score per category.
We emphasised the importance of authenticity in embedding ESG into a firm's culture. This was demonstrated through analysis of peer behaviour and assessing ESG integration into the peers' purpose. A set of recommendations were made to increase the maturity of ESG within the client, including specific frameworks and metrics to start tracking.
Our results:
The board members at the client were able to see where they stood versus their competitors, in more detail than ever before. This detail enabled a set of specific next steps to be laid out around establishing the ESG philosophy and policy of the client, which ESG areas to prioritise, changes to the risk appetite statement to incorporate ESG risks, and making a commitment to becoming net-zero.
Helping Adjoint gain ISO 27001 information security certification to support its expansion strategy
26/02/2024Regulation,Strategy,Data & Analytics,Risk Management,Case Study,Risk,Data,Governance,PeopleFinTech
Adjoint required ISO certification to comply with legislation, across multiple jurisdictions, and increase confidence in their brand. Due to the nature of their clients (fortune 500 and international companies), a widely recognised accreditation was required. The firm's incorporation of next generation processing, such as distributed ledger technology (DLT), increased the complexity to achieve certification. Their global teams in the UK, Switzerland and USA, were undergoing a heavy scaling-up.
We were asked to customise and implement an ISO 27001 framework for global accreditation in IT security management.
Our approach:
- Capture delivery requirements
- Create relevant policies, procedures and a controls framework, for applicable IT functions
- Perform gap analysis and risk assessment
- Establish clear roles and responsibilities and deliver a formal training program
- Conduct internal assurance audit to identify incidents and data breaches
- Lead external certification process with BSI, through Stage 1 and 2 completion
- Provide agile delivery through to completion
Our results:
- Effective coverage of all ISMS mandatory requirements surrounding ISO 27001
- A new performance management system to track controls in company processes, structure and focal points
- Global delivery, with clear road-mapping structure
- Scaled offerings in open APIs and raised brand in the market
- Improved sales process due to meeting client ISO requirements
Helping Adjoint, a DLT FinTech, with agile delivery management services to increase sales at pace
26/02/2024Regulation,Strategy,Change Leadership,Case Study,Risk,Data,Governance,PeopleFinTech
Adjoint required an experienced delivery partner to run technical delivery and build and manage client relationships, as well as create a scalable delivery model. They lacked a scalable platform and struggled to educate prospects and clients on the misconceptions between the benefits of DLT versus the noise created by other solutions.
We were asked to be the client and delivery partner, to deliver DLT solutions to fortune 50 clients, including tier 1 banks, insurers, and multinational corporations. The client wanted a scalable platform to manage internal and external work-streams, as well as internal and client resource prioritisation, to ensure better alignment of the product delivery team.
Our approach:
- Structured approach; using an Agile framework to deliver successful client PoCs and projects, whilst balancing PM, BA, Testing and DevOps deliverables
- Collaborative style; seamlessly adding capabilities and bringing delivery assets to the fore, through a low-risk delivery model, with a focus on outcomes
- Hands-on attitude; unravelling DLT, whilst enabling concrete application in treasury, captive insurance, inter-company lending, and securitisation, ensuring common messaging across clients
Our deliverables:
- Business requirements documents (BRDs)
- Testing artefacts
- Quick reference guides (QRGs)
- Support model
- Security policy
- Project plans
- Issue tracker
- Task management
Business benefits:
- Scalable, commercially attractive, and low-risk delivery model
- Optimisation of internal and external resource
- Market-ready DLT solutions with short term delivery timelines
- Recognised as an industry partner to work on value-add business use cases for DLT
- Senior stakeholder management (internal and external)
Helping a leading investment bank improve its client on-boarding processes into a single unified operating model
26/02/2024Strategy,Change Leadership,Investment Banking,Case Study,Risk,Data,Governance,PeopleRegulation
Our client, like many banks, were facing multiple challenges in their onboarding and account opening processes. Scalability and efficiency were two important metrics we were asked to improve. Our senior experts interviewed the onboarding teams to document the current process and recommended a new unified process covering front, middle and back office teams.
We identified and removed key-person dependencies and documented the new process into a key operating manual for global use.
Helping Clarivate Analytics define a financial services (FS) go-to-market strategy for intellectual property data
26/02/2024Regulation,Strategy,Data & Analytics,Case Study,Risk,Data,Governance,PeopleFinTech
We were asked by Clarivate to analyse their IP data and identify where it might be useful in financial services, based on our industry experience. We created and reviewed 39 use cases, interviewed 59 financial services specialists, and reviewed 150 potential partner companies.
We developed four value propositions and recommended 16 projects to execute the strategy.
Helping a global investment bank design & execute a client data governance target operating model
26/02/2024Strategy,Change Leadership,Investment Banking,Data & Analytics,Case Study,Risk,Data,Governance,PeopleRegulation
Our client had a challenge to evidence control of their 2000+ client data elements. We were asked to implement a new target operating model for client data governance in six months. Our approach was to identify the core, essential data elements used by the most critical business processes and start governance for these, including data ownership and data quality.
We delivered business capability models, data governance processes, data quality rules & reporting, global support coverage for 100+ critical data elements supporting regulatory reporting and risk.
Helping a global investment bank reduce its residual risk with a target operating model
26/02/2024Strategy,Change Leadership,Investment Banking,Data & Analytics,Risk Management,Case Study,Risk,Data,Governance,PeopleRegulation
Our client asked us to provide operating model design & governance expertise for its anti-financial crime (AFC) controls. We reviewed and approved the bank’s AFC target operating model using our structured approach, ensuring designs were compliant with regulations, aligned to strategy, and delivered measurable outcomes.
We delivered clear designs with capability impact maps, process models, and system & data architecture diagrams, enabling change teams to execute the AFC strategy.
Helping ARX, a cyber-security FinTech with interim COO services to scale-up their delivery
26/02/2024Regulation,Strategy,Change Leadership,Artificial Intelligence,Case Study,Risk,Data,Governance,PeopleFinTech
We were engaged by ARX to provide an interim COO as they gaining traction in the market and needed to scale their operations to support their new clients. We used our financial services delivery experience to take on UX/UI design, redesign their operational processes for scale, and be a delivery partner for their supply chain resilience solution.
Due to our efforts, ARX were able to meet their client demand with an improved product and more efficient sales & go-to-market approach.
Helping Bloomberg improve its data offering for its customers
26/02/2024Strategy,Data & Analytics,Data Providers,Case Study,Risk,Data,Governance,PeopleRegulation
Bloomberg wanted us to help review and refresh their 80,000 data terms in order to build a clear ontology of related information. We identified & prioritised the core, essential terms and designed new business rules for the data relationships. By creating a system-based approach, we could train the Bloomberg team to continue our work as BAU.
We improved the definitions, domains, and ranges to align with new ontologies, enabling their 300,000 financial services professionals to make more informed investment decisions.
Helping a Japanese investment bank to develop & execute their trading front-to-bank operating model
26/02/2024Strategy,Change Leadership,Investment Banking,Case Study,Risk,Data,Governance,PeopleRegulation
Our client wanted to increase their trading efficiency by improving their data sourcing processes and resource efficiency in a multi-year programme. We analysed over 3,500 data feeds from 50 front office systems and over 100 reconciliations to determine how best to optimise their data.
Streamlining their data usage and operational processes is estimated to save them 20-30% costs over the next five years.
Helping a global consultancy define & execute its UK FinTech Strategy
26/02/2024Regulation,Strategy,Case Study,Risk,Data,Governance,PeopleFinTech
Our client had developed 39 FinTech value propositions and we were asked to assess the propositions and prioritise when, and how, to go to market. We used our financial services experience and FinTech network to plan the best approach, through outreach, warm introductions, and events.
Our approach led to successful introductions with new prospect FinTechs in payments, neo-banks, and crypto firms within four months.
Helping GLEIF build out a new ISO standard for official organisational roles (ISO 5009)
26/02/2024Strategy,Data & Analytics,Data Providers,Case Study,Risk,Data,Governance,PeopleRegulation
GLEIF engaged us as financial services data experts to identify, analyse, and recommend relevant organisational roles for in-scope jurisdictions based on publicly-available laws & regulations. We looked at 12 locations in a four-week proof-of-concept, using automated document processing
Our work helped GLEIF to launch the ISO 5009 in 2022, enabling B2B verified digital signatures for individuals working in official roles. This digital verification speeds up onboarding time and increases trust.
Improving a DLT FinTech's operations enabling rapid scaling in target markets
25/02/2024TestimonialsFinTech,Regulation,Strategy,Change Leadership,Investment Banking,Corporate Banking,Risk Management,Risk,Data,Governance,People
"Leading Point brings a top-flight management team, a reputation for quality and professionalism, and will heighten the value of [our] applications through its extensive knowledge of operations in the financial services sector."
Chief Risk Officer at DLT FinTech
Developing a GTM strategy at a large alternative data provider to break into new financial services markets
25/02/2024TestimonialsFinTech,Regulation,Strategy,Wealth Management,Data Providers,Risk,Data,Governance,People
"Leading Point’s delivery has been head and shoulders above any other consultancy I have ever worked with."
SVP Large Alternative Data Provider
Increasing data product offerings by profiling 80k terms at a global data provider
25/02/2024TestimonialsRegulation,Strategy,Change Leadership,Investment Banking,Wealth Management,Corporate Banking,Data & Analytics,Artificial Intelligence,Data Providers,Risk,Data,Governance,People
“Through domain & technical expertise Leading Point have been instrumental in the success of this project to analyse and remediate 80k industry terms. LP have developed a sustainable process, backed up by technical tools, allowing the client to continue making progress well into the future. I would have no hesitation recommending LP as a delivery partner to any firm who needs help untangling their data.”
PM at Global Market Data Provider
Catch the Multi-Cloud Wave
25/01/2024ArticleRegulation,Cloud,Multi-Cloud,Strategy,Change Leadership,Investment Banking,Wealth Management,Corporate Banking,Risk,Data,Governance,People,Insurance
Charting Your Course
The digital realm is a constant current, pulling businesses towards new horizons. Today, one of the most significant tides shaping the landscape is the surge of multi-cloud adoption. But what exactly is driving this trend, and is your organisation prepared to ride the wave?
At its core, multi-cloud empowers businesses to break free from the constraints of a single cloud provider. Imagine cherry-picking the best services from different cloud vendors, like selecting the perfect teammates for a sailing crew. In 2022, 92% of firms either had or were considering a multi-cloud strategy (1). Having a strategy is one thing. Implementing it is a very different story. It takes meticulous planning and preparation. The potential of migrating from a single cloud provider to a multi-cloud environment can be huge if you are dealing with vast volumes of data. This flexibility unlocks a treasure trove of benefits.
1 Faction - The Continued Growth of Multi-Cloud and Hybrid Infrastructure
Top 4 Benefits
1 Unmatched Agility
Respond to ever-changing demands with ease by scaling resources up or down. Multi-cloud lets you ditch the "one-size-fits-all" approach and tailor your cloud strategy to your specific needs, fostering innovation and efficiency
2 Resilience in the Face of the Storm
Don't let cloud downtime disrupt your operations. By distributing your workload across multiple providers, you create a safety net that ensures uninterrupted service even when one encounters an issue.
3 A World of Choice at Your Fingertips
No single cloud provider can be all things to all businesses. Multi-cloud empowers you to leverage the unique strengths of different vendors, giving you access to a diverse array of services and optimising your overall offering.
4 Future-Proofing Your Digital Journey
The tech landscape is a whirlwind of innovation. With multi-cloud, you're not tethered to a single provider's roadmap. Instead, you have the freedom to seamlessly adapt to emerging technologies and trends, ensuring you stay ahead of the curve.
Cost Meets the Cloud
Perhaps the most exciting development propelling multi-cloud adoption is the shrinking cost barrier. As cloud providers engage in fierce competition, prices are driving down, making multi-cloud solutions more accessible for businesses of all sizes. This cost optimisation, coupled with the strategic advantages mentioned earlier, makes multi-cloud an increasingly attractive proposition. However, a word of caution: While the overall trend is towards affordability, navigating the multi-cloud landscape still requires meticulous planning and cost management. Without proper controls and precise resource allocation, you risk increased expenses and potential setbacks. With increased distribution of data, comes the increased risk of data leakage. Not only must data be protected within each cloud environment, it needs to be protected across the multi-cloud. Data monitoring increases in complexity. As data needs to move between cloud solutions, there may be additional latency risks. These can be mitigated with good risk controls and monitoring.
Kicking Off Your Journey
Ditch single-provider limitations and enjoy flexibility, resilience, and a wider range of services to boost your digital transformation but remember…
Multi-cloud environments can heighten security risks.
Navigate cautiously with proper controls and expert guidance to avoid hidden expenses.
Fierce competition is lowering multi-cloud barriers.
Let Leading Point be your guide, helping you set sail on the multi-cloud journey with confidence and unlock its full potential.
The multi-cloud path isn't without its challenges, but the rewards are undeniable. At Leading Point, we're experts in helping businesses navigate the multi-cloud wave with confidence. Let us help you unlock the full potential of multi-cloud for a more resilient, flexible, and innovative future. So, is your organisation ready to catch the wave? Contact Leading Point today and start your multi-cloud journey!
AI in Insurance - Article 1 - A Catalyst for Innovation
20/12/2023ArticleInsurance,Regulation,Strategy,Change Leadership,Data & Analytics,Artificial Intelligence,Datatomic Ventures,Data Providers,Risk,Data,Governance,People,FinTech
How insurance companies can use the latest AI developments to innovate their operations
The emergence of AI
The insurance industry is undergoing a profound transformation driven by the relentless advance of artificial intelligence (AI) and other disruptive technologies. A significant change in business thinking is gaining pace and Applied AI is being recognised for its potential in driving top-line growth and not merely a cost-cutting tool.
The adoption of AI is poised to reshape the insurance industry, enhancing operational efficiencies, improving decision-making, anticipating challenges, delivering innovative solutions, and transforming customer experiences.
This shift from data-driven to AI-driven operations is bringing about a paradigm shift in how insurance companies collect, analyse, and utilise data to make informed decisions and enhance customer experiences. By analysing vast amounts of data, including historical claims records, market forces, and external factors (global events like hurricanes, and regional conflicts), AI can assess risk with speed and accuracy to provide insurance companies a view of their state of play in the market.
Data vs AI approaches
This data-driven approach has enabled insurance companies to improve their underwriting accuracy, optimise pricing models, and tailor products to specific customer needs. However, the limitations of traditional data analytics methods have become increasingly apparent in recent years.
These methods often struggle to capture the complex relationships and hidden patterns within large datasets. They are also slow to adapt to rapidly-changing market conditions and emerging risks. As a result, insurance companies are increasingly turning to AI to unlock the full potential of their data and drive innovation across the industry.
AI algorithms, powered by machine learning and deep learning techniques, can process vast amounts of data far more efficiently and accurately than traditional methods. They can connect disparate datasets, identify subtle patterns, correlations & anomalies that would be difficult or impossible to detect with human analysis.
By leveraging AI, insurance companies can gain deeper insights into customer behaviour, risk factors, and market trends. This enables them to make more informed decisions about underwriting, pricing, product development, and customer service and gain a competitive edge in the ever-evolving marketplace.
Top 5 opportunities
1. Enhanced Risk Assessment
AI algorithms can analyse a broader range of data sources, including social media posts and weather patterns, to provide more accurate risk assessments. This can lead to better pricing and reduced losses.
2. Personalised Customer Experiences
AI can create personalised customer experiences, from tailored product recommendations to proactive risk mitigation guidance. This can boost customer satisfaction and loyalty.
3. Automated Claims Processing
AI can automate routine claims processing tasks, for example, by reviewing claims documentation and providing investigation recommendations, thus reducing manual efforts and improving efficiency. This can lead to faster claims settlements and lower operating costs.
4. Fraud Detection and Prevention
AI algorithms can identify anomalies and patterns in claims data to detect and prevent fraudulent activities. This can protect insurance companies from financial losses and reputational damage.
5. Predictive Analytics
AI can be used to anticipate future events, such as customer churn or potential fraud. This enables insurance companies to take proactive measures to prevent negative outcomes.
Adopting AI in Insurance
The adoption of AI in the insurance industry is not without its challenges. Insurance companies must address concerns about data quality, data privacy, transparency, and potential biases in AI algorithms. They must also ensure that AI is integrated seamlessly into their existing systems and processes.
Despite these challenges, AI presents immense opportunities. Insurance companies that embrace AI-driven operations will be well-positioned to gain a competitive edge, enhance customer experiences, and navigate the ever-changing risk landscape.
The shift from data-driven to AI-driven operations is a transformative force in the insurance industry. AI is not just a tool for analysing data; it is a catalyst for innovation and a driver of change. Insurance companies that harness the power of AI will be at the forefront of this transformation, shaping the future of insurance and delivering exceptional value to their customers.
Download the PDF article here.
The Consumer Duty Regulation
09/03/2023ArticleInsurance,Regulation,Consumer Duty,Strategy,Wealth Management,Risk Management,Risk,Data,Governance,People,FinTech
Improving outcomes with the Consumer Duty Regulation

How can buy-side retail financial firms improve consumer outcomes and the wider economy?
The FCA introduced new guidelines, rules and policies last year in 2022, comprised as the Consumer Duty Regulation, to ensure products and services are delivered at fair value to customers, as well as a better standard of care. With the recent rise of the cost-of-living crisis, consumers are struggling and are faced with difficult times ahead, including the UK economy. This Duty lays out responsibilities for Boards and senior management within firms, to implement this regulation, to not only benefit consumers, but the wider economy.
In a recent review published by the FCA in January 2023, the FCA identified key areas where firms are meeting obligations, and where areas of improvement are required. As stated in the Policy Statement PS22/9, the FCA would like to see firms make full use of the implementation period of this three-year strategy, to implement the Duty effectively, and that by October 2022, ‘firm’s boards (or equivalent management body) should have agreed their plans for implementing the Duty’ and to have evidenced this, to ‘challenge their plans to ensure they are deliverable and robust’ (Consumer Duty Implementation Plans, FCA, Jan 2023).
This review published by the FCA, helps firms understand the FCA’s expectations, and to work together with firms to ensure the Duty is implemented effectively. The review identified that firms are behind with the implementation of the Duty and need to improve their approach. Three key areas were suggested where firms can focus on for the second half of the implementation period, the first being ‘effective prioritisation of the Duty’ – in order to reduce risk of poor customer outcomes, and to prioritise the implementation plans. The second ‘embedding substantive requirements’, on how firms are over-confident on their plans, and instead should focus on the substantive requirements laid out in the Duty, and review ‘their products and services, communications and customer journeys, they identify and make the changes needed to meet the new standards’ (Consumer Duty Implementation Plans, FCA, Jan 2023). The third area of focus identified was on how firms should work together with other firms, to share information in the distribution chain, to ensure the Duty can be implemented effectively and consistently (Consumer Duty Implementation Plans, FCA, Jan 2023).

What can retail financial firms do to improve and what are the implications of not meeting the Duty requirements?
From the FCA’s recent review, it has been determined there are still many areas by which firms are falling short, which raises the risks of not meeting the Duty obligation deadlines. From the governance aspect, the FCA’s review has established that the board members and senior management teams within firms, have no clearly defined and developed plans in place, neither timings, and lack engagement. When it comes to the plans compiled by firms, the project requirements and timelines are unclear, there is a lack of detail, explanation, and evidence on the implementation of the Duty, including how a firm’s purpose, culture and values are in alignment with the Duty.
Additionally, the review identified that firms also fail to define risks, and internal/external dependencies such as resource planning, budgeting, and technology resources, including working together with third parties, which as a result may impact the implementation plans. Further, firms fail to distinguish mitigation strategies and approaches or methodologies for conducting reviews and gap analysis of products, services, communications, and customer journeys, as part of implementation of the Four Outcomes within the Duty. Firms have also failed to provide in-depth details into the types of data they will require, and how this will be tested, and used, to better understand the customer outcomes, which is another key part of the Duty requirements.
How can Leading Point help to simplify this process?
At Leading Point, our team of expert practitioners can assist the board members and senior managers within retail financial firms, to conduct more in-depth project scope and planning, gap analysis, as well as workflow strategies, and assist to define clear methodologies and approaches to implement the Duty policies and rules. We are fully-equipped to help any organisation that is looking to improve their implementation plans for meeting the Consumer Regulations, to ensure deadlines are met, whilst reducing costs, and risks, with defined mitigation strategies, and enhanced quality of consumer data. This will not only better equip firms with meeting the Duty obligations, but will help to accelerate new business growth, to ensure high-quality products and services are delivered to consumers.
Appendix and Additional Information on the Duty Regulation
What is the Consumer Duty Regulation?
The FCA introduced the Consumer Duty Regulation, and published the Finalised Guidelines FG22/5, along with the Policy Statement PS22/9 in July 2022, which is a ‘standard of care firms should give to customers in retail financial markets’ (FG22/9, p.3).
The FCA states that the purpose of the Consumer Duty (‘the Duty’) is to provide ‘a fairer basis for competition’, to help ‘boost growth and innovation’ (What firms and customers can expect from the consumer duty and other regulatory reforms, FCA (Sept, 2022)).
The Duty is comprised of three key areas: A Consumer Principle; the Cross-Cutting Rules; and the Four Outcomes (FG22/9, p.3). Each of these three key areas focus on how firms should deliver suitable products and services, as well as good outcomes to consumers.
Which firms and who will it impact?
The FG22/5 Guidelines state that the Duty applies ‘across retail financial services’, and that ‘firms should review all examples in this guidance and consider how they may be relevant to their business models and practices’ (FG22/5).
As stated in the FG22/5 Guidance, it is the firms responsibility to identify which rules and principles are applicable to their firm, and ‘what they are required to do’ (FG22/5).
What is the timeline of this Regulation?
It has been proposed for the Duty to be enforced in two-phase implementation periods, the first being by the end of July 2023, whereby the Duty will apply to new and existing products and services that remain for sale or open for renewal, and the second date is by July 2024, whereby the Duty will come fully into force, and will apply to all closed products and services (PS22/9).
The following timeline has been extracted from the Policy Statement – Implementation Timetable (PS22/9):
Implementation Period | Timeline |
Firms’ boards (or equivalent management body) should have agreed their implementation plans and be able to evidence they have scrutinised and challenged the plans to ensure they are deliverable and robust to meet the new standards. Firms should expect to be asked to share implementation plans, board papers and minutes with supervisors and be challenged on their contents. | End of October 2022 |
Manufacturers should aim to complete all the reviews necessary to meet the four outcome rules for their existing open products and services by the end of April 2023, so that they can:
• Share with distributors by the end of April 2023 the information necessary for them to meet their obligations under the Duty (e.g., in relation to the price and value, and products and service outcomes)
| End of April 2023 |
Manufacturers should: • Identify where changes need to be made to their existing open products and services to meet the Duty and implement these remedies by the end of July 2023 | End of July 2023 |
The Duty will apply to all new products and services, and all existing products and services that remain on sale or open for renewal. This gives firms 12 months to implement the new requirements on the bulk of retail financial products and services, benefiting the majority of consumers | End of July 2023 |
The Duty will come fully into force and apply to all closed products and services. This extra 12 months will help those firms with large numbers of closed products and will also help mitigate some of the wider concerns firms raised about the difficulty of applying the Duty to these products (see Chapter 3). | End of July 2024 |
How should firms implement the Consumer Duty Regulation?
According to the Guidance (FG22/5), it is a firm’s responsibility to identify which policies and rules apply and what they will be required to do (FG22/5). In addition to this, the Guidance has dedicated Chapter 10, on the Culture, Governance and Accountability that the Duty sets out for firms to give their customers. This is so that firms shift their focus on customer outcomes, and to ‘review the outcomes of their customers to ensure they are consistent with the Duty’ (PS22/9).
The Guidance (FG22/5) states the following:
- The rules require firms to ensure their strategies, governance, leadership, and people policies (including incentives at all levels) lead to good outcomes for customers. The rules also make clear that we expect customer outcomes to be a key lens for important areas, such as Risk and Internal Audit.
- A firm’s board, or equivalent governing body, should review and approve an assessment of whether the firm is delivering good outcomes for its customers which are consistent with the Duty, at least annually.
- Individual accountability and high standards of personal conduct in firms will ensure that firms are meeting their obligations under the Duty.
The Guidance (FG22/5) outlines four important drivers of culture that firms will need to ensure they deliver on from: Purpose; Leadership; People; and Governance. The Duty will also hold senior managers accountable via the Senior Managers & Certification Regime (SMCR) (FG22/5). A firm’s board will be responsible for the submission of a Board Report, which will be comprised of an assessment of whether the ‘firm is delivering good outcomes for its customers which are consistent with the Duty’ (FG22/5). Firms will also be required to monitor their outcomes, with a key focus of the Duty requiring firms to ‘assess, test, and understand’ and be able ‘to evidence the outcomes their customers are receiving’ (FG22/5), thus firms will be required to identify relevant sources of their data, to ensure they are consistent with meeting the obligations of the Duty, to their customers.
Unlocking the opportunity of vLEIs
08/02/2023ArticleInsurance,People,Regulation,vLEI,LEI,Strategy,Change Leadership,Investment Banking,Wealth Management,Corporate Banking,Data & Analytics,Data Providers,Risk Management,Risk,Data,FinTech,Governance
Streamlining financial services workflows with Verifiable Legal Entity Identifiers (vLEIs)

Source: GLIEF
Trust is hard to come by
How do you trust people you have never met in businesses you have never dealt with before? It was difficult 20 years ago and even more so today. Many checks are needed to verify if the person you are talking to is the person you think it is. Do they even work for the business they claim to represent? Failures of these checks manifest themselves every day with spear phishing incidents hitting the headlines, where an unsuspecting clerk is badgered into making a payment to a criminal’s account by a person claiming to be a senior manager.
With businesses increasing their cross-border business and more remote working, it is getting harder and harder to trust what you see in front of you. How do financial services firms reduce the risk of cybercrime attacks? At a corporate level, there are Legal Entity Identifiers (LEIs) which have been a requirement for regulated financial services businesses to operate in capital markets, OTC derivatives, fund administration or debt issuance.
LEIs are issued by Local Operating Units (LOUs). These are bodies that are accredited by GLEIF (Global Legal Entity Identifier Foundation) to issue LEIs. Examples of LOUs are the London Stock Exchange Group (LSEG) and Bloomberg. However, LEIs only work at a legal entity level for an organisation. LEIs are not used for individuals within organisations.
Establishing trust at this individual level is critical to reducing risk and establishing digital trust is key to streamlining workflows in financial services, like onboarding, trade finance, and anti-financial crime.
This is where Verifiable Legal Entity Identifiers (vLEIs) come into the picture.
What is the new vLEI initiative and how will it be used?
Put simply, vLEIs combine the organisation’s identity (the existing LEI), a person, and the role they play in the organisation into a cryptographically-signed package.
GLEIF has been working to create a fully digitised LEI service enabling instant and automated identity verification between counterparties across the globe. This drive for instant automation has been made possible by developments in blockchain technology, self-sovereign identity (SSI) and other decentralised key management platforms (Introducing the verifiable LEI (vLEI), GLEIF website).
vLEIs are secure digitally-signed credentials and a counterpart of the LEI, which is a unique 20-digit alphanumeric ISO-standardised code used to represent a single legal organisation. The vLEI cryptographically encompasses three key elements; the LEI code, the person identification string, and the role string, to form a digital credential of a vLEI. The GLEIF database and repository provides a breakdown of key information on each registered legal entity, from the registered location, the legal entity name, as well as any other key information pertaining to the registered entity or its subsidiaries, as GLEIF states this is of “principally ‘who is who’ and ‘who owns whom’”(GLEIF eBook: The vLEI: Introducing Digital I.D. for Legal Entities Everywhere, GLEIF Website).
In December 2022, GLEIF launched their first vLEI services through proof-of-concept (POC) trials, offering instant digitally verifiable credentials containing the LEI. This is to meet GLEIF’s goal to create a standardised, digitised service capable of enabling instant, automated trust between legal entities and their authorised representatives, and the counterparty legal entities and representatives with which they interact” (GLEIF eBook: The vLEI: Introducing Digital I.D. for Legal Entities Everywhere, page 2).
“The vLEI has the potential to become one of the most valuable digital credentials in the world because it is the hallmark of authenticity for a legal entity of any kind. The digital credentials created by GLEIF and documented in the vLEI Ecosystem Governance Framework can serve as a chain of trust for anyone needing to verify the legal identity of an organisation or a person officially acting on that organisation’s behalf. Using the vLEI, organisations can rely upon a digital trust infrastructure that can benefit every country, company, and consumers worldwide”,
Karla McKenna, Managing Director GLEIF Americas
This new approach for the automated verification of registered entities will benefit many organisations and businesses. It will enhance and speed up regulatory reports and filings, due diligence, e-signatures, client onboarding/KYC, business registration, as well as other wider business scenarios.
Imagine the spear phishing example in the introduction. A spoofed email will not have a valid vLEI cryptographic signature, so can be rejected (even automatically), saving potentially thousands of £.
How do I get a vLEI?
Registered financial entities can obtain a vLEI from a Qualified vLEI Issuer (QVI) organisation to benefit from instant verification, when dealing with other industries or businesses (Get a vLEI: List of Qualified vLEI Issuing Organisations, GLEIF Website).
A QVI organisation is authorised under GLEIF to register, renew or revoke vLEI credentials belonging to any financial entity. GLEIF offers a Qualification Program where organisations can apply to operate as a QVI. GLEIF maintain a list of QVIs on their website.

Source: GLIEF
What is the new ISO 5009:2022 and why is it relevant?
The International Organisation of Standards (ISO) published the ISO 5009 standard in 2022, which was initially proposed by GLEIF, for the financial services sector. This is a new scheme to address “the official organisation roles in a structured way in order to specify the roles of persons acting officially on behalf of an organisation or legal entity” (ISO 5009:2022, ISO.org).
Both ISO and GLEIF have created and developed this new scheme of combining organisation roles with the LEI, to enable digital identity management of credentials. This is because the ISO 5009 scheme offers a standard way to specify organisational roles in two types of LEI-based digital assets, being the public key certificates with embedded LEIs, as per X.509 (ISO/IEC 9594-8), also outlined in ISO 17442-2, or for digital verifiable credentials such as vLEIs to be specified, to help confirm the authenticity of a person’s role, who acts on behalf of an organisation (ISO 5009:2022, ISO Website). This will help speed up the validation of person(s) acting on behalf of an organisation, for regulatory requirements and reporting, as well as for ID verification, across various business use cases.
Leading Point have been supporting GLEIF in the analysis and implementation of the new ISO 5009 standard, for which GLEIF acts as the operating entity to maintain the ISO 5009 standard on behalf of ISO. Identifying and defining OORs was dependent on accurate assessments of hundreds of legal documents by Leading Point.
“We have seen first-hand the challenges of establishing identity in financial services and were proud to be asked to contribute to establishing a new standard aimed at solving this common problem. As data specialists, we continuously advocate the benefits of adopting standards. Fragmentation and trying to solve the same problem multiple times in different ways in the same organisation hurts the bottom line. Fundamentally, implementing vLEIs using ISO 5009 roles improves the customer experience, with quicker onboarding, reduced fraud risk, faster approvals, and most importantly, a higher level of trust in the business.”
Rajen Madan (Founder and CEO, Leading Point)
Thushan Kumaraswamy (Founding Partner & CTO, Leading Point)
How can Leading Point assist?
Our team of expert practitioners can assist financial entities to implement the ISO 5009 standard in their workflows for trade finance, anti-financial crime, KYC and regulatory reporting. We are fully-equipped to help any organisation that is looking to get vLEIs for their senior team and to incorporate vLEIs into their business processes, reducing costs, accelerating new business growth, and preventing anti-financial crime.
Glossary of Terms and Additional Information on GLEIF
Who is GLEIF?
The Global Legal Entity Identifier Foundation (GLEIF) was established by the Financial Stability Board (FSB) in June 2014 and as part of the G20 agenda to endorse a global LEI. The GLEIF organisation helps to implement the use of the Legal Entity Identifier (LEI) and is headquartered in Basel, Switzerland.
What is an LEI?
A Legal Entity Identifier (LEI) is a unique 20 alphanumeric character code based on the ISO-17442 standard. This is a unique identification code for legal financial entities that are involved in financial transactions. The role of the structure of how an LEI is concatenated, principally answers ‘who is who’ and ‘who owns whom’, as per ISO and GLEIF standards, for entity verification purposes and to improve data quality in financial regulatory reports.
How does GLEIF help?
GLEIF not only helps to implement the use of LEI, but it also offers a global reference data and central repository on LEI information via the Global LEI Index on gleif.org, which is an online, public, open, standardised, and a high-quality searchable tool for LEIs, which includes both historical and current LEI records.
What is GLEIF’S Vision?
GLEIF believe that each business involved in financial transactions should be identifiable with a unique single digital global identifier. GLEIF look to increase the rate of LEI adoption globally so that the Global LEI Index can include all global financial entities that engage in financial trading activities. GLEIF believes this will encourage market participants to reduce operational costs and burdens and will offer better insight into the global financial markets (Our Vision: One Global Identity Behind Every Business, GLEIF Website).
Séverine Raymond Soulier's Interview with Leading Point
07/09/2022VideoInsurance,Regulation,Strategy,Change Leadership,Investment Banking,Wealth Management,Corporate Banking,Data & Analytics,Datatomic Ventures,Data Providers,Risk,Data,Governance,People,FinTech
Séverine Raymond Soulier’s Interview with Leading Point
Séverine Raymond Soulier is the recently appointed Head of EMEA at Symphony.com – the secure, cloud-based, communication and content sharing platform. Séverine has over a decade of experience within the Investment Banking sector and following 9 years with Thomson Reuters (now Refinitiv) where she was heading the Investment and Advisory division for EMEA leading a team of senior market development managers in charge of the Investing and Advisory revenue across the region. Séverine brings a wealth of experience and expertise to Leading Point, helping expand its product portfolio and its reach across international markets.
John Macpherson's Interview with Leading Point
07/09/2022VideoInsurance,Regulation,Strategy,Change Leadership,Investment Banking,Wealth Management,Corporate Banking,Data & Analytics,Data Providers,Risk Management,Risk,Data,Governance,People
John Macpherson’s Interview with Leading Point 2022
John Macpherson was the former CEO of BMLL Technologies; and is a veteran of the city, holding several MD roles at CITI, Nomura and Goldman Sachs. In recent years John has used his extensive expertise to advise start-ups and FinTech in challenges ranging from compliance to business growth strategy. John is Deputy Chair of the Investment Association Engine which is the trade body and industry voice for over 200+ UK investment managers and insurance companies.
Leading Point and P9 Form Collaboration to Accelerate Trade and Transaction Reporting
12/08/2022NewsRegulation,Strategy,Change Leadership,Investment Banking,Data & Analytics,Risk Management,Trade Reporting,Transaction Reporting,Risk,Data,Governance,People
Leading Point and P9 Form Collaboration to Accelerate Trade and Transaction Reporting
Leading Point and Point Nine (P9) will collaborate to streamline and accelerate the delivery of trade and transaction reporting. Together, they will streamline the delivery of trade and transaction reporting using P9’s scalable regulatory solution, and Leading Point's data management expertise. This new collaboration will help both firms better serve their clients and provide faster, more efficient reporting.
London, UK, July 22nd, 2022
P9’s in-house proprietary technology is a scalable regulatory solution. It provides best-in-class reporting solutions to both buy- and sell-side financial firms, service providers, and corporations, such as ED&F Man, FxPro and Schnigge. P9 helps them ensure high-quality and accurate trade/transaction reporting, and to remain compliant under the following regimes: EMIR, MiFIR, SFTR, FinfraG, ASIC, CFTC and Canadian.
Leading Point, a highly regarded digital transformation company headquartered in London, are specialists in intelligent data solutions. They serve a global client base of capital market institutions, market data providers and technology vendors.
Leading Point are data specialists, who have helped some of the Financial Services industry’s biggest players organise and link their data, as well as design and deliver data-led transformations in global front-to-back trading. Leading Point are experts in getting into the detail of what data is critical to businesses. They deliver automation and re-engineered processes at scale, leveraging their significant financial services domain expertise.
The collaboration will combine the power of P9's knowledge of regulatory reporting, and Leading Point’s expertise in data management and data optimisation. The integration of Leading Point’s services and P9's regulatory technology will enable clients to seamlessly integrate improved regulatory reporting and efficient business processes.
Leading Point will organise and optimise P9’s client’s data sets, making it feasible for P9's regulatory software to integrate with client regulatory workflows and reporting. In a statement made by Christina Barbash, Business Development Manager at Point Nine, she claims that, “creating a network of best-in-breed partners will enable Point Nine to better serve its existing and potential clients in the trade and transaction reporting market.”
Andreas Roussos, Partner at Point Nine adds:
“Partnering with Leading Point is a pivotal strategic move for our organization. Engaging with consulting firms will not only give us a unique position in the market, but also allow us to provide more comprehensive service to our clients, making it a game-changer for our organization, our clients, and the industry as a whole.”
Dishang Patel, COO and Founding Partner at Leading Point, speaks on the collaboration:
“We are thrilled to announce that we are collaborating with Point Nine. Their technology and knowledge of regulatory reporting can assist the wider European market. The new collaboration will unlock doors to entirely new transformation possibilities for organisations within the Financial Sector across EMEA.”
The collaboration reflects the growing complexity of financial trading and businesses’ need for more automation for compliance with regulations, whilst ensuring data management is front and centre of the approach for optimum client success. Considering this, the two firms have declared to support organisations to improve the quality and accuracy of their regulatory reporting for all regimes.
About Leading Point
Leading Point is a digital transformation company with offices in London and Dubai. They are revolutionising the way change is done through their blend of expert services and their proprietary technology, modellr™.
Find out more at: www.leadingpoint.io
Contact Dishang Patel, Founding Partner & COO at Leading Point - dishang@leadingpoint.io
About Point Nine
Point Nine (Limassol, Cyprus), is a dedicated regulatory reporting firm, focusing on the provision of trade and transaction reporting services to legal entities across the globe. Point Nine uses its in-house cutting-edge proprietary technology to provide a best-in-class solution to all customers and regulatory reporting requirements.
Find out more at: www.p9dt.com
Contact Head office, Point Nine Data Trust Limited - info@p9dt.com