Excel Ninjas & Digital Alchemists – Delivering success in Data Science in FS

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In February 150+ data practitioners from financial institutions, FinTech, academia, and professional services joined the Leading Point Data Kitchen community and were keen to discuss the meaning and evolving role of Data Science within Financial Services. Many braved the cold wet weather and made it across for a highly productive session interspersed with good pizza and drinks.

Our expert panellists discussed the “wild” data environment in Financial Services inhabited by “Excel Ninjas”, “Data Wranglers” and “Digital Alchemists”. But agreed that despite the current state of the art being hindered by legacy infrastructure and data siloes there are a number of ways to find success.

Here is the Data Kitchen’s ‘Recipe’ for delivering success in Data Science in Financial Services:

1. Delivery is key – There is a balance to strike between experimentation and delivery. In commercial environments, especially within financial services there is a cost of failure. ROI will always be in the minds of senior management, and practitioners need to understand that is the case. This means that data science initiatives will always be under pressure to perform, and there will be limits on the freedom to just experiment with the data.

2. Understand how to integrate with the business – Understanding what ‘good’ delivery looks like for data science initiatives requires an appreciation of how the business operates and what business problem needs to be solved. Alongside elements of business analysis, a core skill for practitioners is knowing how to ‘blend in’ with the rest the business – this is essential to communicate how they can help the business and set expectations. “Data translators” are emerging in businesses in response.

3. Soft skills are important – Without clear articulation of strategy and approach, in language they can understand, executives will often either expect ‘magic’ or will be too nervous to fully invest. Without a conduit between management and practitioners many initiatives will be under-resourced or, possibly worse, significantly over-resourced. Core competencies around stakeholder and expectation management, and project management is needed from data practitioners and to be made available to them.

4. Take a product mindset – Successful data science projects should be treated in a similar way to developing an App. Creating it and putting it on the ‘shelf’ is only the beginning of the journey. Next comes marketing, promotion, maintenance, and updates. Many firms will have rigorous approaches to applying data quality, governance etc. on client products, but won’t apply them internally. Many of the same metrics used for external products are also applicable internally e.g. # active users, adoption rates etc. Data science projects are only truly successful when everyone is using it the way it was intended.

5. Start small and with the end in mind – Some practitioners find success with ‘mini-contracts’ with the business to define scope and, later, prove that value was delivered on a project. This builds a delivery mindset and creates value exchange.

6. Conduct feasibility assessments (and learn from them) – Feasibility criteria need to be defined that take into account the realities of the business environment, such as:

  • Does the data needed exist?
  • Is the data available and accessible?
  • Is management actively engaged?
  • Are the technology teams available in the correct time windows?

If you run through these steps, even if you don’t follow through with a project, you have learned something – that learning needs to be recorded and communicated for future usage. Lessons from nearly 100+ use cases of data science in financial services and enterprises, suggest that implementing toll-gates for entry and exit criteria is becoming a more mature practice in organisations.

7. Avoid perfection - Sometimes ‘good’ is ‘good enough’. You can ‘haircut’ a lot of data and still achieve good outcomes. A lot of business data, while definitely not perfect, is being actively used by the business – glaring errors will have been fixed already or been through 2-3 existing filters. You don’t always need to recheck the data.

8. Doesn’t always need to be ‘wrangled’ – Data Scientists spend up to 80% of time on "data cleaning" in preparation for data analysis but there are many data cleansing tools now in the market that really work and can save a lot of time (e.g. Trifacta). Enterprises will often have legacy environments and be challenged to connect the dots. They need to look at the data basics – an end to end data management process, the right tools for ingestion, normalisation, analysis, distribution and embedding outputs as part of improving a business process or delivering insights.

Our chefs believed Data Science will evolve positively as a discipline in the next three years with more clarity on data roles, a better qualification process for data science projects, application of knowledge graphs, better education and cross pollination of business and data science practitioners and the need for more measurable outcomes. The lessons from failures are key to make the leap to data-savvy businesses.

Just a quick note to say thank you for your interest in The Data Kitchen!

We had an excellent turn out of practitioners from organisations including: Deutsche Bank, JPMorgan, HSBC, Schroders, Allianz Global Investors, American Express, Capgemini, University of East London, Inmarsat, One corp, Transbank, BMO, IHS Markit, GFT, Octopus Investments, Queen Mary University, and more.

And another Thank You to our wonderful panellists!

  • Peter Krishnan, JP Morgan
  • Ben Ludford, Efficio
  • Louise Maynard-Atem, Experian
  • Jacobus Geluk, Agnos.ai

…And Maître De – Rajen Madan, Leading Point FM
We would like to thank our chef’s again and to all participants for sharing plenty of ideas on future topics, games and live solutions.
The next Data Kitchen – ‘Innovative Data-Tech Start-Ups To Watch’ will be on the 22th April 2020!
Register here: https://www.eventbrite.co.uk/e/the-data-kitchen-innovative-data-tech-start-ups-to-watch-tickets-96061391207
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Regulatory Change, Data SME, RegTech Propositions

Analyst with expertise in regulatory analysis and implementation, customer reference data management, and data driven transformation & delivery. Has worked for a number of RegTech start-ups within Capital Markets.
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LIBOR Signals Need for New Approaches to Legal Data

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 The Scope of LIBOR Remediation is the Problem

Time is now of essence – with the end of 2021 deadline looming, financial institutions need to reduce their ‘stock’ of legacy LIBOR contracts to a minimum as a matter of priority, writes Rajen Madan, CEO, Leading Point.

The challenge is of course colossal. Firms need to find every reference to IBORs embedded in every contract they hold; update each contract with fallback provisions / reflect the terms of the alternative reference rate they are migrating to; and communicate the results with clients.

LIBOR’s retirement potentially impacts over $350 trillion of contracts and requires all LIBOR transactions (estimated at over 100 million documents) to be examined and most likely repapered. LIBOR is embedded in every asset class – mortgages and retail loans, to commodities, derivatives, bonds and securities.

It’s estimated that large banks may be exposed to more than 250,000 contracts directly referencing LIBOR maturing after 2021, and indirectly exposed to many thousands more embedded in servicing activities, supplier agreements and such.

Only 15 percent of Financial Institutions are ready to deal with this volume of contract remediation, deal restructuring, and repapering activities required for the scale of their legacy contract back-book; 14 of the world’s top banks expect to spend more than $1.2 billion on the LIBOR transition.


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Firms that have comprehensive visibility of their legal contract information via retained structured data, can avoid 80 percent of the typical repapering process, and focus their efforts on the remaining, critical, 20 percent.

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LIBOR Repapering Not a ‘Find and Replace’ 

The repapering of contracts isn’t as straightforward as a ‘Find and Replace’ on legal terminology referencing LIBOR.

Risks are many including conduct, legal, prudential and regulatory. Consider ‘conduct’ risk. In the UK, the Treating Customers Fairly (TCF) regime is particularly concerned with how customers are affected by firms’ LIBOR transition plans. Before contracts can be updated, firms will need to ensure that LIBOR linked products and services have ‘fair’ replacement rates that operate effectively.

Similarly, there’s prudential risk. When the underlying contracts change, firms may find that the instruments they rely on for capital adequacy purposes may no longer be eligible, potentially even resulting in a sudden drop in a bank’s capital position. Similarly, there are several Counterparty Credit, Market, Liquidity, and Interest Rate Risks that will need to be reflected in firms’ approaches.  
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LIBOR is proving to be a real impetus for financial institutions to use technology that, to be honest, has been available in the marketplace for a long time now.

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Mindset Change is Needed to Manage Legal Data

Most historic repapering exercises have involved hastily identifying the documents impacted, outsourcing the difficult issues to law firms (at huge cost) and throwing manpower (again at substantial cost) at the problem to handle the contract updates and communications with counterparties. The exact same process has been repeated for every repapering project. Despite the substantial costs, many financial institutions still don’t meet the deadline. MiFID II is an example.

With ample evidence of regulators continually tightening their grip on financial institutions through reform – alongside an increasingly dynamic global business environment (e.g. LIBOR, Brexit) – it is time organisations acknowledged and accepted repapering as a ‘business as usual’ activity.

A change in mindset and a smarter approach is needed to manage legal data. Financial institutions need to ensure that LIBOR or indeed any future business repapering exercise does not compromise client well-being or negatively impact client experience. For instance, to accurately model the financial risk firms’ portfolios are exposed to via LIBOR when transitioning to a new rate, they need a way to directly link, say, multiple cash and derivative contracts to a single client. Furthermore, in an environment where most firms are product driven, the scenario of multiple repetitive communications, requests for information and re-papering contract terms looms on the horizon for firms’ customers.

It is heartening to see that LIBOR is beginning to pique the interest of financial institutions to develop a long-term vision to create smarter capabilities that will deliver business advantages in the future.

Stephanie Vaughan, Global Legal AI Practice Director at iManage RAVN and ex-Allen & Overy, recently observed, “LIBOR is proving to be a real impetus for financial institutions to use technology that, to be honest, has been available in the marketplace for a long time now. While they may have dabbled with it in the past, due to the scale of the LIBOR remediation and the constantly changing regulatory challenges, it has finally hit home that such projects are a drain on resources and are delivering no business value.”
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Financial institutions have started every repapering project (e.g. MiFID II, Dodd Frank, Margin Rules) from scratch including going through the entire process of determining the clients, what the terms of engagement are, when the contracts expire and so on.

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Technology Can Make Repapering ‘Business as Usual’

A strategic approach to managing legal data requires all stakeholders in a financial institution to come on board – from business units and the compliance department through to legal operations and the General Counsel. This is instrumental to ensuring genuine cross-functional recognition and support for strategic directional change.

Financial institutions need to build a strong, technology-supported foundation for remediation projects. Thus far, financial institutions have started every repapering project (e.g. MiFID II, Dodd Frank, Margin Rules) from scratch including going through the entire process of determining the clients, what the terms of engagement are, when the contracts expire and so on.

Hereafter, with LIBOR and Brexit, extracting, classifying, storing and maintaining all these data points as structured, base level information on customers on a single technology platform, will provide institutions with capabilities to quickly understand their exposure, assign priorities and flexibly make contractual changes in tune with evolving requirements.

This approach is proven. Firms that have comprehensive visibility of their legal contract information via retained structured data, can avoid 80 percent of the typical repapering process, and focus their efforts on the remaining, critical, 20 percent. Financial institutions will then also be well poised to take advantage of new bolt on capabilities  that leverage artificial intelligence for application to specific use-cases – which in turn can deliver business value from contract search, contract classification, clause management, to real time analytics, contract generation and integration with operational, risk and compliance systems.

The opportunity with more effective legal data management is huge and realisable. Building and incrementally strengthening capability through the strategic and proactive use of technology is potentially the only way for financial institutions to adapt to their new regulatory and business environment.
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The repapering of contracts isn’t as straightforward as a ‘Find and Replace’ on legal terminology referencing LIBOR.

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Leading Point Financial Markets and iManage RAVN are hosting an industry workshop to discuss in more detail some of the issues addressed in this article and understand how smarter Data Management and Enabling Tech Solutions can realistically be used to reduce the cost, risk, and timelines of client outreach and repapering and improve client experience.

Industry practitioners can register interest here: https://leadingpointfm.com/event-regulatory-changes-understanding-the-challenge-with-repapering-and-how-to-reduce-the-cost/

This article is the 1st of a new series exploring the role of Legal Technology in Financial Services. Please stay tuned! https://leadingpointfm.com/insights/
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LIBOR: Manual Approaches are no Longer Enough to Manage FS Legal Data

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The transition away from LIBOR is the biggest contract remediation exercise in Financial Services history – and firms are under prepared.

As the Bank of England and FCA lays out in bold font, in their January 2020 letter to CEOs, “LIBOR will cease to exist after the end of 2021. No firm should plan otherwise.”[1] As a result, Financial Institutions have very little time to reduce their “stock of legacy LIBOR contracts to an absolute minimum before end-2021”.

The challenge is this:

1. Firms have to find every reference to IBORs embedded in every contract they hold.

2. Update each contract with fallback provisions or to reflect the terms of the alternative reference rate they are migrating to.

3. Communicate the results with clients

 

This is much easier said than done due to the sheer scale of the task.

LIBOR’s retirement has the potential to impact over US$ 350 trillion of contracts and will require all LIBOR transactions (estimated at over 100 million documents) to be examined and most likely repapered. LIBOR is embedded in far more than just derivative contracts. Every asset class is affected; from mortgages and retail loans, to commodities, bonds or securities. The resolution of Lehman Brothers after 2008 gives some idea of the scale of the repapering effort for each firm – Lehman was party to more than 900,000 derivatives contracts alone.

The scope of the problem is part of the problem. Hard numbers are difficult to come by as no-one really knows exactly what their exposure is, or how many contracts they need to change.

Current estimates say large banks’ may be exposed to more than 250,000 contracts directly referencing LIBOR maturing after 2021, and indirectly exposed to many thousands more embedded in servicing activities, supplier agreements or more.

Only 15% of Financial Institutions are ready to deal with this volume of contract remediation, deal restructuring, and repapering activities required for the scale of their legacy contract back-book.[2] Fourteen of the world’s top banks expect to spend more than $1.2 billion on the LIBOR transition[3].


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To approach the LIBOR transition manually will likely require years of man-hours and cost millions of dollars, with significant potential for human error

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There are a wide variety of risks to consider.

But it’s not as straightforward as a ‘Find and Replace’ on legal terminology referencing LIBOR. Firms face huge operational, conduct, legal and regulatory risk arising from both the difficulties in managing the vast volumes of complex client contractual documentation but also the downstream impacts of that documentation having been changed.

Conduct Risk: In the UK, the Treating Customers Fairly (TCF) regime is particularly concerned with how customers are affected by firms’ LIBOR transition plans. Before contracts can be updated, firms will need to ensure that LIBOR linked products and services have ‘fair’ replacement rates that operate effectively.[1] Firms will also need to ensure that any changes made are applied across the entire customer ‘class’ to comply with TCF rules and avoid preferential treatment issues.

Legal Risk: There is a huge amount of legal risk arising from disputes in what interest rates should be paid out in amended agreements referencing alternative reference rates.[2] The ISDA protocol expected to be published in Q2 2020 should help with, but not solve, these problems.[3]

This is not to mention the legacy contracts that cannot legally be converted or amended with fallbacks – named by Andrew Bailey at the FCA as the ‘tough legacy’.[4] The UK Working Group on Sterling Risk Free Reference Rates (RFRWG) is due to publish a paper on ‘tough’ legacy contracts in the second half of Q1 2020.[5]

The realism of firms’ assessments of the number of contracts requiring renegotiation should be considered a legal risk in itself – a realised 10% increase in this number would likely incur serious, additional legal fees.

Prudential Risk: When the underlying contracts change, firms may find themselves in a position where suddenly the instruments they rely on for capital adequacy purposes may no longer be eligible - “This could result in a sudden drop in a bank’s capital position.” [6] For similar reasons, there are a number of Counterparty Credit, Market, Liquidity, and Interest Rate Risks that will need to be reflected in firms’ approaches.

Regulatory Risk: Regulators are closely monitoring firms’ transition progress – and they are not happy with what they are seeing. Financial Policy Committee (FPC) stated in January, 2020, has made clear that they are ‘considering’ the supervisory tools that authorities could use to “encourage the reduction in the stock of legacy LIBOR contracts to an absolute minimum before end-2021.”[7] This is regulatory code for ‘we will either fine or increase the capital requirements for firms we judge to be dropping the ball’. The PRA and FCA laid out their expectations for the transition in June 2019 – this is required reading for any LIBOR transition project manager.[8]
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It’s not as straightforward as a ‘Find and Replace’ on legal terminology referencing LIBOR

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What this means for firms is that they need:

1. The capability to quantify their LIBOR exposure – Firms need a good understanding of their LIBOR contractual exposure that can quantify a) firms’ contractual population (i.e. which documents are affected) b) the legal, conduct and financial risk posed by the amendment of those documents

2. The ability to dynamically manage and track this exposure over time – As strategies evolve, the regulatory environment changes, and new scenarios develop, so will firms’ exposure to LIBOR change. Without good quality analytics that can track this effectively, in the context of this massive change project, firms will be strategically and tactically ‘flying blind’ in the face of the massive market shifts LIBOR will bring about.

3. The capability to manage documentation - Jurisdictional, product, or institutional differences will necessitate large client outreach efforts to renegotiate large populations of contracts, manage approvals & conflict resolution, while tracking interim fall-back provisions and front office novation of new products to new benchmarks.

Accomplishing the above will require enterprise-wide contract discovery, digitisation, term extraction, repapering, client outreach and communication capabilities – and the ability to tie them all together in a joined-up way.

To approach the LIBOR transition manually will likely require years of person-hours and cost millions of dollars, with significant potential for human error.


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Accomplishing the above will require enterprise-wide contract discovery, digitisation, term extraction, repapering, client outreach and communication capabilities – and the ability to tie them all together in a joined-up way

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LIBOR cannot be treated as ‘just one more’ repapering exercise.

Firms are continually hit with new requirements which require the update, negotiation and amendment of client contracts.

The reaction is always the same: Scramble to identify the documents impacted, outsource the thornier problems to external legal, and hire huge teams of consultants, remediation armies and legal operations to handle the contract updates and communications with counterparties.

Once complete - often months past the deadline - everyone stands down and goes home. Only to do the same thing again next year in response to the next crisis. While this gets the job done, there are number of problems with this project by project approach:

1. It’s inefficient: Vast amounts of time (and money) is spent just finding the documents distributed around the business, often in hard copy, or locked away in filing cabinets.

2. It’s expensive: External legal, consultants and remediation shops don’t come cheap – especially when the scope of the project inevitably expands past the initial parameters.

3. It’s ineffective: Little to no institutional knowledge is retained of the project, no new processes are put in place, and documents continue to get locked away in filing cabinets - meaning when the time comes to do it again firms have to start from scratch.

When you look at the number of major repapering initiatives over the past 10 years the amount of money spent on repapering projects is monumental. In the EU alone, regulations such as MiFID II, EMIR, GDPR, PPI, FATCA, Brexit and AIFMD have each required a huge repapering project. In 2020, LIBOR, Initial Margin Rules and SFTR will each require contract remediation programmes.

Doing ‘just another’ repapering exercise for LIBOR is a risky mistake. There is a better way.

Smarter data management and enabling tech solutions can help identify, classify and extract metadata from the huge volumes of LIBOR impacted documents at speed. The ability to extract and store contractual information as structured information at this scale allows firms’ the essential capabilities to understand and track their LIBOR exposure, assign priorities and maintain flexibility in a changing situation.

Firms that have fuller visibility of their legal contract information, retained as structured data, can avoid 80% of the typical repapering process, and focus their efforts on the remaining, critical, 20%.[1] The time spent manually identifying contractual needs, can be reallocated to the areas that matter, freeing up legal resource, budget, and project timelines – while simultaneously improving client relationships.

This should not be seen just as a repapering enabler, but a strategic capability. The opportunities afforded through data mining firms’ contractual estate for analytics are vast.


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Doing ‘just another’ repapering exercise for LIBOR is a risky mistake. There is a better way

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One possibility is the ability to connect contracts directly to trades. To accurately model the financial risk firms’ portfolios are exposed to via LIBOR when transitioning to a new rate, they will need a way to directly link, for example, multiple cash and derivative contracts to a single client. Firms are still a long way from this capability – but there are a growing number of sophisticated artificial intelligence solutions that can begin to address these types of use-cases.

Firms that build these capabilities now will materially reduce their risk exposures, improve liquidity and funding, build trust with their clients and be much better equipped to meet other pressing regulatory requirements such as Brexit, SFTR, CRD 5/6, Initial Margin (IM) rules, QFC and more.
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Regulatory Change, Data SME, RegTech Propositions

Analyst with expertise in regulatory analysis and implementation, customer reference data management, and data driven transformation & delivery. Has worked for a number of RegTech start-ups within Capital Markets.
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Leading Point Financial Markets and iManage RAVN are hosting an industry workshop to discuss in more detail some of the issues addressed in this article and understand how smarter Data Management and Enabling Tech Solutions can realistically be used to reduce the cost, risk, and timelines of client outreach and repapering and improve client experience.

 

Industry practitioners can register interest here: https://leadingpointfm.com/event-regulatory-changes-understanding-the-challenge-with-repapering-and-how-to-reduce-the-cost/

This article is the 1st of a new series exploring the role of Legal Technology in Financial Services. Please stay tuned! https://leadingpointfm.com/insights/

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[1] ‘Next steps on LIBOR transition’, January 2020, FCA & PRA https://www.fca.org.uk/publication/correspondence/dear-smf-letter-next-steps-libor-transition.pdf
[2] 2019 LIBOR Survey: Are you ready to transition?, September 2019, Accenture. https://www.accenture.com/_acnmedia/109/Accenture-2019-LIBOR-Survey-fixed.pdf#zoom=50
[3] ‘The end of Libor: the biggest banking challenge you've never heard of’, October 2019, Reuters.
[4] Firms will also need to consider whether any contract term they may rely on to amend a LIBOR-related product is fair under the Consumer Rights Act 2015 (the CRA) in respect of consumer contracts. FG18/7: Fairness of variation terms in financial services consumer contracts under the Consumer Rights Act 2015 contains factors that firms should consider when thinking about fairness issues under the CRA when they draft and review unilateral variation terms in their consumer contracts. https://www.fca.org.uk/markets/libor/conduct-risk-during-libor-transition
[5] Litigation risks associated with Libor transition: https://collyerbristow.com/longer-reads/litigation-risks-associated-with-libor-transition/
[6] UK Working Group on Sterling Risk-Free Reference Rates (RFR WG) 2020 Top Level Priorities. https://www.bankofengland.co.uk/-/media/boe/files/markets/benchmarks/rfr/rfrwgs-2020-priorities-and-milestones.pdf?la=en&hash=653C6892CC68DAC968228AC677114FC37B7535EE
[7] LIBOR: preparing for the end, https://www.fca.org.uk/news/speeches/libor-preparing-end
[8]  UK Working Group on Sterling Risk-Free Reference Rates (RFR WG) 2020 Top Level Priorities. https://www.bankofengland.co.uk/-/media/boe/files/markets/benchmarks/rfr/rfrwgs-2020-priorities-and-milestones.pdf?la=en&hash=653C6892CC68DAC968228AC677114FC37B7535EE
[9] Letter from Sam Woods: The prudential regulatory framework and Libor transition, Bank of England, https://www.bankofengland.co.uk/-/media/boe/files/prudential-regulation/letter/2019/prudential-regulatory-framework-and-libor-transition.pdf?la=en&hash=55018BE92759217608D587E3C56C0E205A2D3AF4
[10] ‘Next steps on LIBOR transition’, January 2020, FCA & PRA https://www.fca.org.uk/publication/correspondence/dear-smf-letter-next-steps-libor-transition.pdf
[11] ‘Firms’ preparations for transition from London InterBank Offered Rate (LIBOR) to risk-free rates (RFRs): Key themes, good practice, and next steps.’, June 2019, FCA & PRA https://www.bankofengland.co.uk/-/media/boe/files/prudential-regulation/publication/2019/firms-preparations-for-transition-from-libor-to-risk-free-rates.pdf?la=en&hash=EA87BD3B8435B7EDF25A56C932C362C65D516577
[12] MiFID II – the long tail of legal documentation repapering, https://www.fintechfutures.com/2018/04/mifid-ii-the-long-tail-of-legal-documentation-repapering/

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Intersecting the Old World with the New

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It has always been a challenge for large corporations to adopt change.  There is constant change being experienced at all institutions but, despite the appetite for change, the size of an organisation often hamstrings its ability to execute on innovative initiatives.

So, what differentiates those who can deliver successful change versus those who cannot?  In one word: Execution!

Execution is the biggest differentiator between small, agile and nimble businesses and their much larger counterparts.  Even if you put to one side the classic large corporate roadblocks (such as organisational complexity and bureaucracy), it’s clear that those who decide to take the leap of faith and try to change the world by starting their own businesses seem to be able to avoid and, often, ignore convention to deliver significant change.

Innovation in large organisations must pass through many layers of change management and control which frequently ties the hands of those who are the agents of change. Equally frequently, organisational politics have an adverse impact.  This is not true of ‘Upstarts’.

‘Upstarts’ break the glass ceiling of ‘the norm’ to create change by significantly improving an existing system or reinventing a process, convention, etc.  But one must question why it is easier for Upstarts to achieve significant change where larger organisations struggle and fail to achieve the same success.

Is it because Upstarts have more skills or able people to execute change?  Probably not, although one must believe Upstart people form a more focused collective.  It’s much simpler than that – it’s a matter of having the time and inclination to apply that collective focus to the achievement of a single objective.  Having, as a sole objective, creating and delivering industry augmenting technology will result in an executable product roadmap and realistic delivery timelines.
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Execution is the biggest differentiator between small, agile and nimble businesses and their much larger counterparts
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However, there is one area in which large corporates have the upper hand: domain expertise.  Upstarts, by virtue of their size, generally do not have the breadth of expertise of larger organisations.  There are many Upstarts who are capable and indeed do produce top of the line tech solutions.  However, often these same single solutions providers (focus!) struggle to appreciate and navigate the vast array of problems large FS organisations are looking to address. Due to these information gaps, solutions can often result in not being fully fit for purpose and therefore hinder an Upstarts ability to precisely satisfy the needs of large FS corporates.

In addition, large organisations have deep pockets.  This allows them to research and develop solutions internally or to attract external innovation by setting up Innovation Labs, or both.  The main objective of these Labs is to experiment with and identify the kind of innovation that will create competitive advantage.  Upstarts may find themselves part of the Innovation Lab or even acquired in the process.

While Innovation Labs may ensure large players don’t get left behind, there is a big opportunity being missed.  This is the integration of external innovation with internal processes and capabilities.  Acquisitions should be aligned with internal use cases i.e. known (or guessed at) issues with existing business workflows such as efficiency gains.  The main reason seems to be that each is located in its own silo.
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Having, as a sole objective, creating and delivering industry augmenting technology will result in an executable product roadmap and realistic delivery timelines
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So internal use cases (areas in need of improvement and change) are not connected to potential external innovative solutions.  And this is not to speak of the bigger challenge which is to identify those use cases in the first place.  This raises a number of questions:

  • Does the right type of resource exist?
  • Can available internal staff ask the right questions?
  • Is an independent party better placed to conduct such an exercise?
  • Will this be prevented by internal politics?
  • Who’s going to pay for the work?
  • Who’s going to take ownership?
  • ... and the list goes on.

Successful organisations engage the right people at the right level internally as well as identify and breakdown the ability of Upstarts to address wide ranging and often long-standing issues.  This takes a certain type of skill set including

  • The ability to face off across the corporate spectrum
  • Applying the correct level of domain expertise and insight, and
  • The ability and expertise to collaborate with Upstarts; to name but a few.

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Entrepreneurs, especially the good ones, know that if failure is to happen, it happens fast.  This requires the ability to EXECUTE. 
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The common thread: entrepreneurship. Why?

Entrepreneurs, especially the good ones, know that if failure is to happen, it happens fast.  This requires the ability to EXECUTE.  Getting the job done is very high on the agenda for any entrepreneur.  Lateral and cohesive thinking is also vital.  Steve Jobs once said, “creativity is just merging things” and entrepreneurs do this better than anyone and tend to find ways through means others don’t or won’t pursue through such approaches as marginal gains.

Entrepreneurs don’t have all the answers. Not at all.  But to bridge the gap between larger, more conventional-minded organisations and newer Upstarts, one must have the ability to “intersect the old world with the new”.  An excellent example of this was the event we ran Data Innovation Uncovered and the work we continue to do in the FinTech space including in Enterprise Blockchain and Client Lifecycle Management.

We love to talk about this intersection and encourage free and open conversation so please feel free to get in touch to share your thoughts or indeed to hear more of ours.
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To bridge the gap between larger, more conventional-minded organisations and newer Upstarts, one must have the ability to “intersect the old world with the new”
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Event: Law, Data & Machines: How can AI reduce the cost of client repapering?

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The deadline for LIBOR is fast approaching.

Many financial institutions are struggling with their re-papering exercise and are facing similar challenges due to the sheer size, scale and scope of the problem.

Firms are under huge pressure from regulators, clients and their competitors to manage their contractual obligations more effectively:

  • Volume – The sheer volume of legal documentation to track numbers in the millons for many institutions
  • Effort – Low levels of automation and digitisation of legal workflow and processes means high no. of resources and spend on professional services
  • Spend – Current and ongoing major client documentation remediation projects such as Brexit, LIBOR, and QFC
  • Risk – Inability to track legal documentation across silos, products, or clients leads to legal, reputational, and operational risk.

Leading Point FM and iManage are hosting an industry innovation session on the use of machine learning software to automate the review and repapering of documents affected by this and other remediation projects.

Participants will be guided by leading industry practitioners on topics including:

  • De-mystifying how technology can be used in the Finance Industry and the wider use cases it solves
  • Key challenges to implementation and how to overcome them
  • The immediate and future benefits that can be gained in this approach

At this event you have an opportunity to benchmark your organisation’s approach to LIBOR and other regulations that require repapering against your peers with practitioners from the Legal, COO, and Change & Compliance sectors.

Date: 26th February 2020
Venue: City Point, 1 Ropemaker Street, London, EC2Y 9AW
Time: 16:00-18:00 followed by drinks & networking

 
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The Data Kitchen | Does data need 'science'?

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Does data need science?

There is a huge skills gap in the data landscape. Europe needs 346,000 more data scientists in 2020 for the 47% of EU organisations struggling to fill data science roles.*

Financial Services is primed to take advantage of what data science can deliver in a wide variety of use-cases like risk analytics, customer behaviour analytics, fraud detection, customer data management, algorithmic trading, natural language services – to name just a few.

However, when an estimated 70% - 85% of data science projects fail, only 20% of analytics insights deliver business outcomes, and 80% of AI projects “remain alchemy, run by wizards”, it is clear that data science is no guarantee of business success.**

Often the problems faced by organisations don’t need data science to solve. Just because your business holds unstructured data, does it need to be analysed? When Data Scientists spend up to 80% of time on "data cleaning" in preparation for data analysis, statistical modelling, & machine learning it is clear that data science/analytics aren't yet ready for self-service at the vast majority of companies.

Companies who find success with data science have a very clear idea of what they want to do with data. They think very hard about how to collect it, how to store it, how to process it, what kinds of analyses they want to perform on it, what they want to do with the results, and how to communicate the results to stakeholders.

There is a wide world of data professionals who understand that focus on the data ‘fundamentals’ i.e. quality, structure, accessibility is essential to business outcomes. Many organisations are missing these fundamentals.

So, in an environment where the skills are difficult to find and expensive to hire, ROI is hard to realise, and the data to be studied is dirty or non-existent – Does data really need science?

*Oreilly Media, 2019, Evolving Data Infrastructure

** Gartner, 2019, Gartner Top Strategic Predictions for 2020 and Beyond

 

Come to the Data Kitchen to discuss:

• What does Data Science really mean in the Financial Services context? Is there a common definition?

• How does data engineering improve the speed and quality of data scientists' work?

• Are there best practices to delivering ROI in data science in Financial Services?

• Are traditional data roles being superseded by ‘scientists’?

Why should you come?

Learn from experienced FS practitioners about how they have delivered business initiatives with data as an asset

Share ideas on how you can position yourself in the dynamic world of data

Network with like-minded people from FS, Fintech, VC’s, and Data-Innovators and gain insights on career paths

Whether you are a recent graduate, a businessperson, a technologist, a data scientist or just work on data initiatives, come along to meet like-minded people.

 

Introducing The Data Kitchen

Food | The Kitchen is where people gather to eat, drink and spend time with family. But it is also a place of work. You can do both at the Data Kitchen.

Data | The Data Kitchen is a community of people interested in, or working with, data in Financial Services. Every event focuses on a different theme related to data as an asset or data as a liability and related innovative topics within Financial Services.

Community | We aim to provide a warm, conversational atmosphere to share ideas, learn and network over drinks and hot food. Our events are free and open to everyone.

Insight | The events are a great opportunity to listen to fascinating insight on the modern developments in data-innovations, AI, DLT in the industry through experiences of FS and Fintech professionals.

Tickets are available on a 1st come, 1st served basis.

Future Events

April 2020 Burrito vs Pastry chef - Does data need to be fancy?

June 2020 Farm to table for Data - How important are your data sources?

August 2020 Data Lasagne – Many cheesy layers?

October 2020 The Data Practitioners Cookbook
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Leading Point FM featured in IFTA 2019 Trade Handbook

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Leading Point has been featured in the “India Fintech Awards - IFTA 2019” Trade Handbook highlighting 30 UK fintechs with a strong presence or interest in the Indian market. We are proud to be selected alongside Transferwise, Revolut, R5FX and Onfido. This demonstrates our ongoing partnerships with the East and sets us up for an exciting 2020!

Our team was part of the Fintech Trade Mission to India in May-June 2019 with the London Mayors Office and will be participating again in 2020.

 

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Leading Point FM CEO Rajen Madan on Fintech Focus TV

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Leading Point FM Founder & CEO Rajen Madan has been featured on Harrington Starr's Fintech Focus TV where he discussed the company's capabilities and solutions, the world of data, problems in the FS industry. He provides an overall picture of new technology and  trends in the market with a growing focus on delivery and execution in companies!
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Leading Point FM successfully delivers ISO 27001 certification for Fintech client

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Leading Point led the certification process for external global accreditation ISO 27000 for one of their leading global Distributed Ledger Technology clients. ISO 27001 is an international Information Security Management System standard providing best practice in improving information security and also compliance with GDPR. The COO of Adjoint found Leading Point's steer in Stage 1 & 2 delivery and execution invaluable: 

"We partnered with Leading Point Financial Markets to define and implement our ISMS. Adjoint selected to partner with Leading Point on this project because they have extensive experience within the FinTech industry, this coupled with the expertise and experience within the information security domain, Leading Point were the obvious choice for this project. What should not be overlooked is Leading Point's influence and recommendations in deciding the most appropriate certification to go for as well as guidance on what ongoing maintenance would be best suited for our needs and match with those of the global certification provider."

 
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About Leading Point Financial Markets:

Leading Point FM is a data-driven business solutions provider for transformative plays in Financial Services. It works with FinTech, RegTech, DataTech on the one hand and established Financial Institutions to deliver smoother, cost effective business operations. Global financial institutions use Leading Point FM for its Think Fast design, domain data assets and unique ability to deploy ML, AL and DLT in functions such as Client Lifecycle Management, Compliance, Legal, Risk and Data Analytics.
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About Adjoint:

Adjoint Treasury is a powerful platform that unifies your treasury workstation, accounting record, and bank systems to enable real-time payments & settlements, cash management & flexible intercompany loans. Gain visibility, increase control and allow for instantaneous transfers without cumbersome reconciliations.
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The Data Kitchen: 'Data & Risk: Have you left the stove on?'

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In our Data Kitchen event we reviewed the Cyber Security threat landscape, market trends and covered key questions regarding Information Security. It was a highly interactive session with insights on:

-        Where does Info Security fit in the organization in Fintech, Established Financial Institutions and what does the Info Security Solution Ecosystem look like

-        What role does tech play in Info Security?

-        What are the key business Data Risk challenges and focus for the next 5 years?

-        Where are the future opportunities and risk mitigation

 

The Recipe’ for tackling InfoSec problems in the rapidly evolving digital innovation landscape in financial services includes seven ingredients

  1. Culture – Cyber is not just a technology problem, it is a people problem. You cannot mitigate cyber risks with just technology; embeddin g the right culture in your people is vital.
  2. Knowing what is important – Data is expanding exponentially – you have to know what you need to protect. Only by defining importan t data, reducing the data “signal noise” and joining multiple data up can organisations look to protect it.
  3. Speed means risk – The speed of innovation is often faster than the ‘speed’ of safety. Understanding that as the world moves towards ‘real-time’, batch-processing operating models will be left behind. As technology and data adoption is rapidly changing, data protection has to keep up as well – there is little point in investing in technology until you really understand your risks and your exposure to those risks. This is increasingly true of new business-tech frameworks, including DLT, AI and Open Banking. In turn this should inform product design and business strategy of Fintech.
  4. Master the Basics – 80% of UK companies and startups are not even Cyber Essentials ready, which shows that real fundamentals are not bei ng dealt with. Larger companies are rigid and not sufficiently agile – more demands are being placed on teams but without sufficient resources and skills development. Large companies cannot innovate if they are not given the freedom to actually adapt.
  5. Collaboration – Plenty of innovative startups are created day-by-day and more to follow in the future. Large businesses need to be more open to collaborating with them to help speed up advancements in the Cyber Risk space.
  6. Technology Plays a Key Role – Emerging operating models are more open API’s Fintech, and organisations need to stitch together many poi nt solutions. Tech can help here if deployed correctly. To join up multiple data. To provide transparency of messages cross in and out of systems.  To execute and detect information security processes and controls with 100X efficiency and speed. This is material in the new world of FS.
  7. Take a holistic view of your data risk – Cyber risk (especially regarding data protection) is simultaneously a compliance problem (regul atory risk, legal risk etc.), an architecture problem (infrastructure, business continuity, etc.), and a business problem (reputational risk, loss of trust, ‘data poisoning’, competitor intelligence etc.). i.e. You can have the best Cyber Security software in the world, but it is worthless if your customer data server’s power supply is exposed on the outside of the building. There are existing risk assessment frameworks for managing operational risk (example: ORMF) – why not plug in?

Just a quick note to say thank you for your interest in The Data Kitchen!

We had an excellent turn out of practitioners from organisations including: Deutsche Bank, Lloyds Banking Group, Star Compliance Inc, Panaseer, JPMorgan, HSBC, Kubrick Group, Macquarie Group, Bank of America, Standard Chartered Bank, FinchCapital, Adjoint Inc, ICE, QVC, Flux and Mizuho at the latest Data Kitchen last week.

For our second Data Kitchen we thought our ‘chefs’ brought out some brilliant ingredients and insights from their experiences in addressing Cyber Security strategically, representing Fintech, Institutional, Investor and Tech viewpoints.

  • David Reynolds, SEED VC
  • Gary Wong, Razor Risk
  • Somil Goyal, Adjoint

…And Maître De - Rajen Madan, Leading Point FM

 We would like to thank our chef’s again and to all participants for sharing plenty of ideas on future topics, games and live solutions.

The next Data Kitchen – Does data need ‘science’? will be on the 20th Feb 2020!

Register here: https://www.eventbrite.co.uk/e/the-data-kitchen-does-data-need-science-tickets-84178635565

We are also proud to present a sequel to this Data Kitchen: Data & Risk ‘Have you left the stove on?’ for June 2020, where we will be bringing 5 high potential cybersecurity starts-ups to pitch and showcase their solutions – Stay tuned!
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IR35 & YOU


The Data Kitchen | Data & Risk: Have you left the stove on?

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Data & Risk: Have you left the stove on?


Data, Data, Data. Whether it's 'Big', 'Structured', 'Personal' or 'Meta', in a lake, silo or vault - More data was created in the last 5 years than in the entirety of human history. Whether it’s on your smartphone, excel, social media or tableau, people are interacting with more and more data every day.

The rapid adoption of new technology, such as artificial intelligence or the cloud has provided more and more complex data risks for organisations. While addressing concerns on data privacy and the ethical use of data are now priorities, businesses continue to digitise and become more data driven.

With many (and increasing) high profile data breaches, Information Security & Cyber risk are now (or should be) board level priorities.

Come to the Data Kitchen to discuss:

  • What are the key business data risk challenges in the next 5 years?
  • How does (or should) Information Security fit within organisations?
  • What is working, what is not?
  • How do you know your data infrastructure is fit for purpose?
  • Where does technology come into the picture?

Why should you come?

  • Learn from experienced FS practitioners about how they have delivered business initiatives with data as an asset
  • Share ideas on how you can position yourself in the dynamic world of data
  • Network with like-minded people from FS, Fintech, VC’s, and Data-Innovators and gain insights on career paths
  • Whether you are a recent graduate, a businessperson, a technologist, a data scientist or just work on data initiatives, come along to meet like-minded people.

Introducing The Data Kitchen


Food | The Kitchen is where people gather to eat, drink and spend time with family. But it is also a place of work. You can do both at the Data Kitchen.

Data | The Data Kitchen is a community of people interested in, or working with, data in Financial Services. Every event focuses on a different theme related to data as an asset or data as a liability and related innovative topics within Financial Services.

Community | We aim to provide a warm, conversational atmosphere to share ideas, learn and network over drinks and hot food. Our events are free and open to everyone.

Insight | The events are a great opportunity to listen to fascinating insight on the modern developments in data-innovations, AI, DLT in the industry through experiences of FS and Fintech professionals.

Tickets are available on a 1st come, 1st served basis.

Future Events


November 2019 Hester Blumenthal or Hairy Biker - Does data need 'science'?

January 2020 Burrito vs Pastry chef - Does data need to be fancy?

February 2020 Farm to table for Data - How important are your data sources?

March 2020 Data Lasagne – Many cheesy layers?

April 2020 The Data Practitioners Cookbook

 

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Leading Point FM Shortlisted For Data Management Insight Awards

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Leading Point Financial Markets has been shortlisted for the A-Teams Data Management Insight Awards.

Data Management Insight Awards, now in their seventh year, are designed to recognise leading providers of data management solutions, services and consultancy within capital markets.

Leading Point has been nominated for four categories:

  1. Most Innovative Data Management Provider
  2. Best Data Analytics Solution Provider
  3. Best Proposition for AI, Machine Learning, Data Science
  4. Best Consultancy in Data Management

View the short list of our competition. 

Areas of Outstanding Service & Innovation

Leading Form Index: Data readiness assessment, created by Leading Point FM, which measures firms data capabilities and their capacity to transform across 24 unique areas. This allows participating firms to understand the maturity of their information assets, the potential to apply new tech (AI, DLT) and benchmark with peers.

Chief Risk Officer Dashboard: Management Information Dashboard that specifies, quantifies, and visualises risks arising from firms’ non-financial, operational, fraud, financial crime, and cyber risks.

Leading Point FM ‘Think Fast’ Application: The application provides the ability to input use cases and solution journeys and helps visualise process, systems and data flows, as well as target state definition & KPI’s. This allows business change and technology teams to quickly define and initiate change management.

Anti-Financial Crime Solution: Data centric approach combined with Artificial Intelligence technology reimagines and optimises AML processes to reduce volumes of client due diligence, reduce overall risk exposure, and provide the roadmap to AI-assisted automation.

Treasury Optimisation Solution: Data content expertise leveraging cutting edge DLT & Smart Contract technology to bridge intracompany data silos and enable global corporates to access liquidity and efficiently manage finance operations.

Digital Repapering Solution: Data centric approach to sourcing, management and distribution of unstructured data combined with NLP technology to provide roadmap towards AI assisted repapering and automated contract storage and distribution.

Leading Form Practical Business Design Canvas: A practical business design method to describe your business goals & objectives, change projects, capabilities, operating model, and KPI’s to enable a true business-on-a-page view that is captured within hours.

ISO 27001 Certification - Delivery of Information Security Management System (ISMS) & Cyber risk mitigation with a Risk Analysis Tool

 
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24th September: Leading Point FM Presenting to AIMA on Practical Challenges of the LIBOR transition

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EVENT: 24th September, 2019

Leading Point FM in partnership with Linklaters, DRS and AIMA are briefing buy-side hedge funds and asset managers on the practical challenges they face in the transition away from LIBOR at an AIMA membership briefing.

Interbank offered rates (IBORs) play a central role in financial markets and act as reference rates to hundreds of trillions of dollars in notional of derivatives and trillions of dollars in bonds, loans, securitizations and deposits.

The FCA will no longer seek require banks to submit quotes to the London Interbank Offered Rate (LIBOR) – LIBOR will be unsupported by regulators come 2021, and therefore, unreliable. The rate is referenced by over $350trillion of existing financial products. It is instrumental in the derivatives, credit and bond markets as well as being embedded in modelling and accounting systems. LIBOR’s replacement is the largest task the financial markets have ever faced - the clock is ticking.

Please join Leading Point FM and market experts from a wide range of disciplines to discuss the challenges of the cessation of LIBOR.:
- The regulatory backdrop to LIBOR’s demise
- The size and scope of the challenge
- The new risk-free rate replacements
- The practical challenges and opportunities posed by the end of LIBOR

This event is open to AIMA members only. Please contact AIMA here: https://www.aima.org/events.html

 
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Speakers -

Nick Railton Edwards - Head of Research, DRS

Mark Brown - Derivatives and Structured Products Partner, Linklaters

Rajen Madan – Founder & CEO, Leading Point Financial Markets
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Artificial Intelligence & Anti-Financial Crime

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Introduction

Leading Point Financial Markets recently hosted a roundtable event to discuss the feasibility of adopting Artificial Intelligence (AI) for Anti-Financial Crime (AFC) and Customer Lifecycle Management (CLM).

A panel of SMEs and an audience of senior execs and practitioners from 20+ Financial Institutions and FinTechs discussed the opportunities and practicalities of adopting data-driven AI approaches to improve AFC processes including KYC, AML, Payment Screening, Transaction Monitoring, Fraud & Client Risk Management.
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EXECUTIVE SUMMARY

AFC involves processing and analysing vast volume and variety of data; it’s a challenge to make accurate & timely decisions from it.

Industry fines, increasing regulatory requirements, a steep rise in criminal activities, cost pressures and legacy infrastructures is putting firms under intense pressure to up their game in AFC.

90% expressed the volume and quality of data as a top AFC/CLM challenge for 2019.

Applying standards to internal data and client documents were deemed as quick wins to improving process

80% agreed that client risk profiling and the analysis across multiple data sources can be most improved - AI can improve KPI’s on False Positives, Client Risk, Automation & False Negatives.

While the appetite for AI & Machine Learning is increasing but firms need to develop effective risk controls pre-implementation

Often the end to end process is not questioned; firms need to look beyond the point tech, and define the use case for value

Illuminating anecdotes shared on how to make the business case for AI/ Tech. Business, AFC Analysts and Ops have different needs

Firms face a real skills gap in order to move from a traditional AFC approach to an intelligent-data led one. Where are the teachers?
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60% of respondents had gone live with AI in at least one business use-case or were looking to transition to an AI-led operating model

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AI & Anti-Financial Crime 

Whether it is a judgement on the accuracy of a Client’s ID, an assessment of the level of money laundering risk they pose, or a decision on client documentation, AI has the potential to improve accuracy and speed in a variety of areas of the AFC and CLM process.

AI can help improve speed and accuracy of AFC client verification, risk profiling, screening and monitoring with a variety approaches. The two key ways AI can benefit AFC are:

  • Process automation – AI can help firms in taking the minimum number of steps and the data required to assemble a complete KYC file, complete due diligence, and to assign a risk rating for a client
  • Risk management – AI can help firms better understand and profile clients into micro-segments, enabling more accurate risk assessment, reducing the amount of false positives that firms have to process

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Holistic examination of the underlying metadata assembled and challenging AI decisions will be necessary to prevent build up of risk and biases

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Mass retraining will be necessary when AI becomes more integral to businesses

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KYC / Customer Due Diligence (CDD)

Key challenge: How can anti-money laundering (AML) operations be improved through machine learning?

Firms’ KYC / CDD processes are hindered by high volumes of client documentation, the difficulty in validating clients’ identity and the significant level of compliance requirements

AI can link, enrich and enhance transactions, risk and customer data sets to create risk-intelligence allowing firms to better assess and predict clients’ risk rating dynamically and in real-time based on expected and ongoing behaviour - this improves both the risk assessment and also the speed of onboarding

AI can profile clients through the use of entity resolution which establishes confidence in the truth of the clients identity by matching them against their potential network generated by analysis of the initial data set provided by client

Better matches can be predicted by deriving additional data from existing and external data sources to further enhance scope & accuracy of client’s network

The result is a clear view of the client’s identity and relationships within the context of their environment underpinned by the transparent and traceable layers of probability generated by the underlying data set
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To improve data quality, firms need to be able to set standards for their internal data and their client’s documentation

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82% of respondents cited ‘Risk Analysis & Profiling’ as having the most opportunity for improvement through AI

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If documentation is in a poor state, you've got to find something else to measure for risk – technology that provides additional context is valuable

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Transaction Screening

Key pains faced by firms are the number of false positives (transactions flagged as risky that are subsequently found to be safe), the resulting workload in investigating them, as well as the volume of ‘false negatives’ (transactions that are flagged as risky, but released incorrectly)

AI can help improve the accuracy and efficiency of transaction and payment screening at a tactical and strategic level

Tactically, AI can reduce workload by carrying out the necessary checks and transactions analysis. AI can automate processes such as structuring of the transaction, verification of the transaction profile and discrepancy checks

Strategically, AI can reduce the volume of checks necessary in the first place by better assessing the client’s risk (i.e., reducing the number of high risk clients by 10% through better risk assessment reduces the volume of investigatory checks).

AI can assist in automating the corresponding investigative processes, which are currently often highly manual, email intensive with lots of to-and-fro.
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A ‘White List’ of transactions allows much smoother processing of transactions compared to due diligence whenever a transaction is flagged

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82% of respondents cited ‘Risk Analysis & Profiling’ as a key area that could be most improved by AI applications

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Transaction Monitoring

Firms suffer from a high number of false positives and investigative overhead due to rules-based monitoring and coarse client segmentation

AI can help reduce the number of false positives and increase the efficiency of investigative work by allowing monitoring rules to target more granular types of clients (segments), updating the rules according to client’s behaviour, and intelligently informing investigators when alerts can be dispositioned.

AI can expand the list of features that you can segment clients on (e.g. does a retailer have an ATM on site?) and identify the hidden patterns that associate specific groups of clients (e.g., Client A, an exporter, is transacting with an entity type that other exporters do not). It can use a firm’s internal data sources and a variety of external data sources to create enriched data intelligence.

Reinforcement learning allows firms to adjust their own algorithms and rules for specific segments of clients and redefine those rules and thresholds to identify correlations and deviations, so different types of clients get treated differently according to their behaviour and investigative results
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Survey Results

90% of respondents to Leading Point FM’s survey on AI and Anti-Financial Crime cited ‘Volume & Quality of Data’ as being one of the top 3 biggest challenges for CLM and AFC functions in 2019

82% of respondents to cited ‘Risk Analysis & Profiling’ as having the most opportunity for improvement through AI

60% of respondents had gone live with Artificial Intelligence in at least one business use case or were looking to transition to an AI-led operating model.

However, 40% were unclear on what solutions were available 60% of respondents cited ‘Immaturity of Technology’ or ‘Lack of Business Case’ as the biggest obstacle to adopting AI applications
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Conclusion

To apply AI practically requires an understanding of the sweet spot between automation and assisting, leveraging human users’ knowledge and expertise

AI needs a well-defined use case to be successful as it can’t solve for all KYC problems at the same time. In order to deliver value, clarity on KPI’s that matter and reviewing AI considering the end-to-end business process is important.

Defining the core, minimal data set needed to support a business outcome, meet compliance requirements, and enable risk assessment will help firms make decisions on what existing data collection processes/ sources are needed, and where AI tech can support enrichment. It is possible to reduce data collection by 60-70% and significantly improve client digital journeys.

There are significant skills gaps in order to move from a traditional AFC op model to more intelligent-data AI led one. When AI becomes more integral to business, mass re-training will be necessary. So, where are the teachers?

The move from repetitive low value-added tasks to more intelligent-data based operating models. Industry collaborations & standards will help, but future competitive advantage will be a function of what are you doing with data that no one else is.
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70% of respondents cited ‘Effort. Fatigue & False Positives’ as one of the top 3 biggest challenges for CLM and AFC functions in 2019?

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More data isn’t always better. There is often a lot of redundant data that is gathered unnecessarily from the client.

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Spotting suspicious activity via network analysis can be difficult if you only have visibility to one side of the transactions

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If there's a problem worth solving, any large organisation will have at least six teams working on it – it comes down to the execution

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The Data Kitchen: From Ingredients to Recipe

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We had an excellent turn out on Tuesday to Leading Point FM's inaugural Data Kitchen event!

Practitioners from organisations such as: 10X Banking, LSE, Brevan Howard, Scotiabank, Legal & General, Liberum, Zercuity, Kings College, Oxford, Barclays, JPMorgan, Fundscape, Deutsche Bank, Nomura, Adjoint, Citi, UBS, IHS Markit, Consilience, LHV Bank, Capital on Tap.

We thought our panellist ‘chefs’ brought out some brilliant insights from their experiences on how to build a personal brand in their careers as entrepreneurs, data leaders, innovators, VC and business executives.

‘The Recipe’ for building your personal brand in the rapidly evolving data landscape in financial services is:

  1. Delivery – People associate you with the outcomes you deliver and your ability to help others meet their goals and commitments.
  2. Use data to support ‘the mission’ – One of the biggest weaknesses that face professionals regarding data is the ability to tell a story around what it *really* means to their audience.
  3. Communicate – Communicate with relevant stakeholders in business terms and relate the data to the business’ pain or gain.
  4. Focus on the end-user/client – Ultimately, they are the arbiter of your success
  5. Do what you enjoy! - Confidence and passion come from finding what you are good at and success will follow.
  6. Find your ‘quirk’ – Embrace the thing that makes you different. People remember your quirks and respect authenticity.
  7. Balance ‘Fail fast’ with perseverance – People shouldn’t apply ‘fail fast’ mentality to building a bridge. Some things require perseverance, planning, problem solving and delivery.
  8. Experimentation – Knowing which skills and roles fit you best is a matter of trial and error. Take on a variety of roles and see which fits best. You’ll grow in skills and capabilities. Always up-skill and re-skill as the situation and market changes.

We had plenty of ideas from the community on topics, games and live solutions for the next Data Kitchen - Watch this space!

Register interest for the October Session here: https://bit.ly/2Yx5t7T

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The Data Kitchen: Data - A Key Ingredient For Your Personal Brand

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Data - A key Ingredient for Your Personal Brand.

 

Data, Data, Data. Whether it's 'Big', 'Structured', 'Personal' or 'Meta', in a lake, silo or vault - More data was created in the last 5 years than in the entirety of human history. Whether it’s on your smartphone, excel, social media or tableau, people are interacting with more and more data every day.

Financial services and Fintechs are actively looking at ways to be more data-led businesses, unlock revenue from their data, better support their client journeys, meet regulatory requirements and manage the risks and liabilities involved in handling the sheer volume and variety of data these days. What’s important and what’s not? How do you distinguish yourself from the crowd? How do you build a career in data-led businesses?

Come to the Data Kitchen for an informal, non-technical discussion with experienced financial services and fintech professionals on how they have practically delivered business outcomes using data as an asset, and how they have grown their personal brand and career with data.

Why should you come?

  • Learn from experienced FS practitioners about how they have delivered business initiatives with data as an asset
  • Share ideas on how you can position yourself in the dynamic world of data
  • Network with like-minded people from FS, Fintech, VC’s, and Data-Innovators and gain insights on career paths

Whether you are a recent graduate, a businessperson, a technologist, a data scientist or just work on data initiatives, come along to meet like-minded people.

 

Participants include: 

 

Peter Krishnan | JPMorgan

Ieva Elvyra Kazakeviciute | Revolut

Milan Kutmutia | Deutsche Bank

Rajen Madan | Leading Point Financial Markets

 

Introducing The Data Kitchen

 

Food | The Kitchen is where people gather to eat, drink and spend time with family. But it is also a place of work. You can do both at the Data Kitchen.

Data | The Data Kitchen is a community of people interested in, or working with, data in Financial Services. Every event focuses on a different theme related to data as an asset or data as a liability and related innovative topics within Financial Services.

Community | We aim to provide a warm, conversational atmosphere to share ideas, learn and network over drinks and hot food. Our events are free and open to everyone.

Insight | The events are a great opportunity to listen to fascinating insight on the modern developments in data-innovations, AI, DLT in the industry through experiences of FS and Fintech professionals.

Tickets are available on a 1st come, 1st basis.

 

Future Events

 

September 2019 Data is brewing

October 2019 Hester Blumenthal or Hairy Biker - Does data need 'science'?

November 2019 Data & Risk: Have you left the stove on?

January 2020 Burrito vs Pastry chef - Does data need to be fancy?

February 2020 Farm to table for Data - How important are your data sources?

March 2020 Data Lasagne – Many cheesy layers?

April 2020 The Data Practitioners Cookbook

 
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Falsely Positive - Event Highlights

 

Leading Point Financial Markets hosted a roundtable event to discuss the feasibility of adopting Artificial Intelligence (AI) for Anti-Financial Crime (AFC) and Customer Lifecycle Management  (CLM). A panel of SMEs and an audience of senior execs and practitioners from 20+ Financial Institutions and FinTechs undertook a detailed discussion on the opportunity and practicality of adopting data-driven AI approaches to solve business issues AFC business issues.

The discussion centred AI solutions’ feasibility within 3 key AFC process examples, Customer Due Diligence (CDD), Transaction Screening & Transaction Monitoring. Participants attending included: Financial Conduct Authority, JP Morgan, HSBC, Deutsche Bank, Credit Suisse, Morgan Stanley, Barclays, BAML, UBS, Santander, CBA, as well as the Financial Times and a variety of challenger FinTechs.

We’ll be releasing a report of the event, including survey results of the audience, over the next few weeks – so watch this space. If you weren’t able to attend and would like a copy of the report – please take our survey and leave your email address here - https://lnkd.in/dt-Tgcp


Leading Point FM forges strategic AI partnership for Anti-Financial Crime

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Specialist Data-Driven Business Solutions company Leading Point FM will apply and augment Daric’s Artificial Intelligence (AI) platform to provide lean, accelerated solutions for Anti-Financial Crime to serve both established Financial Institutions and challenger FinTechs.

Firms are facing huge pressures to improve their Anti-Financial Crime (AFC) capabilities in 2019. Large volumes of data, growing regulatory requirements, poor data quality, high compliance & operating costs and customer digital experience are the most common pain points that firms experience.

With Daric’s AI capabilities, Leading Point FM will enable firms to harness the power of ML and AI to identify customer risk, dynamically screen payments against transaction history, automate periodic reviews and enhanced due-diligence processes and embed risk intelligence. The combined approach will give financial institutions deeper understanding of their clients, reduce false positives, assure compliance for new regulations and improve business KPI’s.

The partnership comes ahead of the 5th EU Money Laundering Directive, and multiple, billion-dollar fines handed from U.S and UK authorities.

“By combining our expertise with Daric’s AI and expanding our practice in London with new industry hires, we are equipping our clients with an integrated approach to Anti-Financial Crime, one that is based on real process re-imagining, domain expertise, data assets and AI-powered customer insights rather than point solutions and remediation projects which don’t deliver any meaningful business results. There is a great opportunity for firms to address money laundering, fraud prevention and cyber risk requirements with a holistic approach on the underlying data – this is precisely what the Leading Point and Daric partnership make uniquely possible for firms with substantial savings.” said Rajen Madan during a high profile trade mission of leading UK fintech companies led by the Mayor of London’s International Business Programme, with other leading UK fintech companies to India to explore growth opportunities with investors, FinTechs and Industry Leaders at the Barclays Rise Accelerator in Mumbai.

Daric CTO & Co-Founder Vasant Ramachandran added:

“We are thrilled to be working with Leading Point FM to deliver this end-to-end value proposition and increase the impact of our technology for Anti Financial crime use-cases such as client intelligence, transaction monitoring and risk management. This will allow our clients to drive automation across their workflows and to adopt risk-augmented models based upon data intelligence. Technology and process re-imagining go hand in hand.”
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About Leading Point Financial Markets:

Leading Point FM is a data-driven business solutions provider for transformative plays in Financial Services. It works with FinTech, RegTech, DataTech on the one hand and established Financial Institutions to deliver smoother, cost effective business operations. Global financial institutions use Leading Point FM for its Think Fast design, domain data assets and unique ability to deploy ML, AL and DLT in functions such as Client Lifecycle Management, Compliance, Legal, Risk and Data Analytics. 
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About Daric:

Daric uses Machine Learning and Artificial Intelligence to improve client digital journeys, real time transaction screening, fraud prevention and risk intelligence for banks and financial institutions. The company is based in Santa Clara, California. Their technology team includes veterans of Goldman Sachs, Palantir Technologies, Google, and LinkedIn, and their investors include industry leaders including Wells Fargo CEO Richard Kovacevich and Goldcrest Investments.
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LIBOR Transition - Preparation in the Face of Adversity

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LIBOR TRANSITION IN CONTEXT

What is it?  FCA will no longer seek require banks to submit quotes to the London Interbank Offered Rate (LIBOR) – LIBOR will be unsupported by regulators come 2021, and therefore, unreliable

Requirement: Firms need to transition away from LIBOR to alternative overnight risk-free rates (RFRs)

Challenge: Updating the risk and valuation processes to reflect RFR benchmarks and then reviewing the millions of legacy contracts to remove references to IBOR

Implementation timeline: Expected in Q4 2021
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HOW LIBOR MAY IMPACT YOUR BUSINESS

Front office: New issuance and trading products to support capital, funding, liquidity, pricing, hedging

Finance & Treasury: Balance sheet valuation and accounting, asset, liability and liquidity management

Risk Management: New margin, exposure, counterparty risk models, VaR, time series, stress and sensitivities

Client outreach: Identification of in-scope contracts, client outreach and repapering to renegotiate current exposure

Change management: F2B data and platform changes to support all of the above
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WHAT YOU NEED TO DO

Plug in to the relevant RFR and trade association working groups, understand internal advocacy positions vs. discussion outcomes

Assess, quantify and report LIBOR exposure across jurisdictions, businesses and products

Remediate data quality and align product taxonomies to ensure integrity of LIBOR exposure reporting

Evaluate potential changes to risk and valuation models; differences in accounting treatment under an alternative RFR regime

Define list of in-scope contracts and their repapering approach; prepare for client outreach
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“[Firms should be] moving to contracts which do not rely on LIBOR and will not switch references rates at an unpredictable time”

Andrew Bailey, CEO,
Financial Conduct Authority (FCA)
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“Identification of areas of no-regret spending is critical in this initial phase of delivery so as to give a head start to implementation”

Rajen Madan, CEO,
Leading Point FM

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BENCHMARK TRANSITION KEY FACTS
  • Market Exposure - Total IBOR market exposure >$370TN 80% represented by USD LIBOR & EURIBOR
  • Tenor - The 3-month tenor by volume is the most widely referenced rate in all currencies (followed by the 6-month tenor)
  • Derivatives - OTC and exchange traded derivatives represent > $300TN (80%) of products referencing IBORs
  • Syndicated Loans - 97% of syndicated loans in the US market, with outstanding volume of approximately $3.4TN, reference USD LIBOR. 90% of syndicated loans in the euro market, with outstanding volume of approximately $535BN, reference EURIBOR
  • Floating Rate Notes (FRNs) - 84% of FRNs inthe US market, with outstanding volume of approximately $1.5TN, reference USD LIBOR. 70% of FRNs in the euro market,with outstanding volume of approximately $2.6TN, reference EURIBOR
  • Business Loans - 30%-50% of business loans in the US market, with outstanding volume of approximately $2.9TN, reference USD LIBOR. 60% of business loans in the euro market, with outstanding volume of approximately $5.8TN, reference EURIBOR

*(“IBOR Global Benchmark Survey 2018 Transition Roadmap”, ISDA, AFME, ICMA, SIFMA, SIFMA AM, February 2018)
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Event: Falsely Positive - Is AI the Silver Bullet for Anti-Financial Crime?

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ADDRESSING THE AFC BUSINESS CHALLENGES


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Firms are facing huge pressure to improve their Anti-Financial Crime (AFC) capabilities in 2019 across fraud, cyber, AML, sanctions, data security, ABAC.

The combination of new and evolving regulation, firms’ operational complexity and increases in cyber crime have led to dissatisfied clients, poor risk management, massive compliance costs and increased competition from challenger banks.

This situation is only getting worse as shortage of expertise, cultural resistance to change, and depleted internal resources create barriers to digital transformation in a fragmented solutions marketplace.

Maturing Artificial Intelligence (AI) technology is being positioned as ‘the answer’ - some estimating it can reduce costs in AFC by as much as 47%.*

*Autonomous, April 2018, “Machine Intelligence & Augmented Finance”
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 “There is no question that AI shows great promise in the long term – it could transform our industry…”

Rob Gruppetta,
Head of Financial Crime, Financial Conduct Authority (FCA)
Nov 2018
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HOW CAN DATA-DRIVEN AI APPROACHES HELP?


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[AI] has the potential to slash the costs of the [regulatory] challenge … by reducing false positives in monitoring systems and redirecting the efforts of human experts to other, more productive, areas

World Economic Forum,
Jan 2019
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What outcomes do organisation stakeholder groups seek from their Client Journey, Risk Management and AFC Operating Models?

Business: Reduce onboarding and maintenance bottlenecks to accelerate timelines and improve client journeys, enable data-driven granular understanding of clients.

COO & CTO: Improve the efficiency, accuracy, and adaptability of screening and transaction monitoring workflows to provide efficiency gains and improved KPIs.

Regulatory & Compliance: Automate governance of complex models and reporting tools to support regulatory review and put compliance in the front-line

Risk Management: Segment clients according to contextual and transactional behaviour to better evaluate emerging AFC threats and improved risk thresholds
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HOW CAN FIRMS GRASP THE AI OPPORTUNITY?

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How can organisations grasp the AI opportunity? Is AI a Silver Bullet or a Red-Herring?

What best practices and practical implementation insights are available for organisations to up their game in client digital journeys, risk management & AFC?

An executive workshop of leaders & practitioners from Business, CIO, COO, AFC and Change will assess the opportunity of adopting data-driven AI approaches and discuss practical ways to solve business issues related to AML / AFC. Whether you are an AI skeptic, evangelist or pragmatist, attend this session to:

  • Understand AI and how it relates to AFC, Client Journey & Risk Management
  • See 3 core business use cases of AI in action with live solutions
  • Understand how to structure the implementation journey & pitfalls to avoid

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1. Understand the business challenges & where you can up your game in AFC?

2. Live solutions: 3 core AFC business use cases with AI

3. How to remove barriers & implement with success
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Data Innovation, Uncovered

 

Leading Point Financial Markets recently partnered with selected tech companies to present innovative solutions to a panel of SMEs and an audience of FS senior execs and practitioners across 5 use-cases Leading Point is helping financial institutions with. The panel undertook a detailed discussion on the solutions’ feasibility within these use-cases, and their potential for firms, followed by a lively debate between Panellists and Attendees.

EXECUTIVE SUMMARY

“There is an opportunity to connect multiple innovation solutions to solve different, but related, business problems”

  • 80% of data is relatively untapped in organisations. The more familiar the datasets, the better data can be used
  • On average, an estimated £84 million (expected to be a gross underestimation) is wasted each year from increasing risk and delivery from policies and regulations
  • Staying innovative, while staying true to privacy data is a fine line. Solutions exist in the marketplace to help
  • Is there effective alignment between business and IT? Panellists insisted there is a significantly big gap, but using business architecture can be a successful bridge between the business and IT, by driving the right kinds of change
  • There is a huge opportunity to blend these solutions to provide even more business benefits

CLIENT DATA LIFECYCLE (TAMR)

  • Tamr uses machine learning to combine, consolidate and classify disparate data sources with potential to improve customer segmentation analytics
  • To achieve the objective of a 360-degree view of the customer requires merging external datasets with internal in a appropriate and efficient manner, for example integrating ‘Politically Exposed Persons’ lists or sanctions ‘blacklists’
  • Knowing what ‘good’ looks like is a key challenge. This requires defining your comfort level, in terms of precision and probability based approaches, versus the amount of resource required to achieve those levels
  • Another challenge is convincing Compliance that machines are more accurate than individuals
  • To convince the regulators, it is important to demonstrate that you are taking a ‘joined up’ approach across customers, transactions, etc. and the rationale behind that approach

LEGAL DOCS TO DATA (iManage)

  • iManage locates, categorises & creates value from all your contractual content
  • Firms hold a vast amount of legal information in unstructured formats - Classifying 30,000,000 litigation documents manually would take 27 years
  • However, analysing this unstructured data and converting it to structured digital data allows firms to conduct analysis and repapering exercises with much more efficiency
  • It is possible to a) codify regulations & obligations b) compare them as they change and c) link them to company policies & contracts – this enables complete traceability
  • For example, you can use AI to identify parties, dates, clauses & conclusions held within ISDA contract forms, reports, loan application contracts, accounts and opinion pieces

DATA GOVERNANCE (Io-Tahoe)

  • Io-Tahoe LLC is a provider of ‘smart’ data discovery solutions that go beyond traditional metadata and leverages machine learning and AI to look at implied critical and often unknown relationships within the data itself
  • Io-Tahoe interrogates any structured/semi-structured data (both schema and underlying data) and identifies and classifies related data elements to determine their business criticality
  • Pockets of previously-hidden sensitive data can be uncovered enabling better compliance to data protection regulations, such as GDPR
  • Any and all data analysis is performed on copies of the data held wherever the information security teams of the client firms deems it safe
  • Once data elements are understood, they can be defined & managed and used to drive data governance management processes

FINANCIAL CRIME (Ayasdi)

  • Ayasdi augments the AML process with intelligent segmentation, typologies and alert triage. Their topological data analysis capabilities provide a formalised and repeatable way of applying hundreds of combinations of different machine learning algorithms to a data set to find out the relationships within that data
  • For example, Ayasdi was used reason-based elements in predictive models to track, analyse and predict complaint patterns. over the next day, month and year.
  • As a result, the transaction and customer data provided by a call centre was used effectively to reduce future complaints and generate business value
  • Using Ayasdi, a major FS firm was able to achieve more than a 25% reduction in false positives and achieved savings of tens of millions of dollars - but there is still a lot more that can be done

DATA MONETISATION (Privitar)

  • Privitar’s software solution allows the safe use of sensitive information enabling organisations to extract maximum data utility and economic benefit
  • The sharp increase in data volume and usage in FS today has brought two competing dynamics: Data protection regulation aimed at protecting people from the misuse of their data and the absorption of data into tools/technologies such as machine learning
  • However, as more data is made available, the harder it is to protect the privacy of the individual through data linkage
  • Privitar’s tools are capable of removing a large amount of risk from this tricky area, and allow people to exchange data much more freely by anonymisation
  • Privitar allows for open data for innovation and collaboration, whilst also acting in the best interest of customers’ privacy

SURVEY RESULTS

  • Encouragingly, over 97% of participants who responded confirmed the five use cases presented were relevant to their respective organisations
  • Nearly 50% of all participants who responded stated they would consider using the tech solutions presented
  • 70% of responders believe their firms would be likely to adopt one of the solutions
  • Only 10% of participants who responded believed the solutions were not relevant to their respective firms
  • Approximately 30% of responders thought they would face difficulties in taking on a new solution

Innovation is Not Perfect. Accept and Embrace It

ThushanThushan Kumaraswamy
Partner at Leading Point Financial Markets

 

It was my pleasure to attend Societe Generale's breakfast event on 9 November 2018 called "Implementing New Technologies" in Spitalfields, London on behalf of Leading Point Financial Markets. The event comprised of presentations about the FinTech innovation landscape and the use of Robotics Process Automation (RPA) in SocGen, followed by a panel discussion, hosted by Susanne Chishti, Founder of FinTech Circle.

Since there was so much good content and thinking at this event, I thought I would share my views on the event and how it ties to our propositions at Leading Point Financial Markets.

Do not ignore FinTech companies that are not 100% ready

There are thousands of FinTech (and RegTech, LegalTech, WealthTech, InsureTech, XYZTech!) companies just in the UK, let alone globally. Many of these are in different stages of their evolution.

Start-up Lifecycle

Source: The Startup Lifecycle

Financial services firms, especially larger firms, often resist adopting innovative technologies from companies who don't have a long record of existing clients. In such a fast-moving environment as FinTechs, this can mean losing out on the potential business benefits at a time when competition is squeezing margins and ever-increasing regulatory pressures are driving up costs.

Imagine being able to run a pilot or proof-of-concept for a small area of the business, with an identified strategy of goals and specific objectives, to demonstrate to the senior management team how such a new technology could be used to deliver real business benefits. This kind of pilot can be run in an agile fashion, but require business and IT teams are fully on-board and involved with the project. Since the scope is small, the resource commitment is also smaller than a normal implementation.

There is a significant opportunity for financial services firms who are willing to start these small-scale projects with innovation companies who might not be 100% ready (in the Validating or Scaling phases above) alongside implementation partners who know the technology, have the domain knowledge and understand operating models.

Don't automate a bad process

Robotics Process Automation (RPA) as a concept is easy enough to understand; computer programs (the "robots" or "bots"), using a set of pre-defined rules replicate what humans would do using computer systems in a repetitive fashion. For example, daily copying of client names from an Excel sheet to a CRM (Customer Relationship Management) system. This basic automation can free up the human workers to do more valuable work.

Rapid evolution of robotics

Source: Robots Join The Team

This is all good stuff. However, before jumping straight to implementing RPA solutions, it is worth considering what the business process is actually doing. Is this Excel-to-CRM method the best way of getting client details into the CRM system? Is it possible to improve the process first? As part of an RPA implementation, you should be looking at process improvement strategies first, then automating what is left. This way, you save on the number of bots you would need and increase the efficiency of the process as a bonus. Process experts can document existing processes and identify opportunities for improvement prior to any RPA technology implementation.

How does a bot change a password when accessing a core system?

There are some potential gotchas when using bots, like the above question, which can cause problems during day-to-day running. If a bot uses a specific login to access a core system and that login has a password expiry, what happens then? Is the bot expected to define a new password? Should a human get involved? Also, consider licences on existing software platforms; are there any clauses that prevent the use of bots? There may not be right now, but it is not difficult to foresee software companies bringing in new clauses to control the potential uptick of system usage through bots.

Panel Discussion: Selecting and Implementing New Technologies

Panel discussion

  • Susanne Chishti, Founder of FinTech Circle (Host)
  • Anthony Woolley, Head of Innovation, Societe Generale
  • Vasu Vasudevan, Digital Enablement Capbility Lead, Schroders
  • Richard Archer, Director, EY
  • Keith Phillips, Executive Director, The Investment Association and Velocity

The first question was about trends in innovation. The guests talked about the bleed of innovation between FinTechs, RegTechs, LegalTechs, but also into manufacturing and other industry sectors. The biggest topics being:

  • Artifical Intelligence (AI) and Machine Learning (ML)
  • Big Data
  • Cloud
  • Distributed Ledger Technology (DLT) / Blockchain
  • Social & Mobile
  • Robotics & Automation

As mentioned above, the twin drivers of competition shrinking margins and regulatory compliance increasing costs are forcing companies to come up with new ways of thinking. This may not come naturally to the larger, older financial services firms. They may have pockets of innovation but sometimes struggle to create a company-wide innovation culture.

Chalkbaord

The importance of customer-centricity was raised to a question on technological advancements. Building a single view of client will enable improved service to clients and increased revenue growth using data analysis across large cross-referenced data sets to be more specific with marketing and cross-selling.

An interesting question about how to bridge the gap between legacy platforms and new innovations was put to the panel next. It was noted that capacity is required to do this. How do companies get that capacity? By using technologies like RPA to free up people to generate this real value for the business.

Another technique is to use APIs (Application Programming Interfaces) as wrappers around your legacy platforms to make them easier to connect to other, more modern, applications. Using APIs turns your legacy platforms into building blocks that be linked together. A COBOL API can let other systems use the data held in the COBOL system, without the need for expensive COBOL programmers.

Intro to APIs

Source: Intro to APIs

This brings additional data protection concerns though, as customer data held in these legacy platforms may not have up-to-date data security and data protection applied to them and exposing the data through APIs could potentially increase risk of data loss.

A concern raised by the panel was about the use of RPA as a concrete sticking plaster rather than as a purely temporary fix for the use of legacy technology. The temptation is there once an RPA solution is doing its work, to leave it there rather than address the legacy platform.

The panel were asked about their top three technologies. The answers covered:

  • Data aggregation, clustering & consolidation
  • AI and ML
  • Blockchain
  • Data analytics (behavioural analysis for active asset management)
  • Digital passports (recording clients' digital identities)
  • Intelligent automation (robotics)
  • Unstructured to structured data
  • Document intelligence (text mining)
  • RPA
  • Collaboration tools in investment operations
  • Natural language processing (voice recognition)
  • Cloud (along with data and APIs)
Emerging Techs

Source: Top 30 Emerging Technologies

One important factor for digital was considering how people interacted with their devices. Many people of a certain age feel comfortable using on-screen keyboards and touch gestures. Some younger people prefer voice interactions through assistants like Alexa, Siri or Google and that audience is only going to grow.

A vital question was put to the panel about how to implement new technologies. FinTechs often feel like they are in a zoo. Potential clients come to see what they can do, have some meetings, but then don't connect again. There are some activities that can improve the relationship-building on both sides for FinTechs trying to scale-up or break into financial services; along with the obvious (but not always followed) things like respecting each other and being collaborative, there is a need to not destroy the start-up's spirit. Go in to the relationship with the understanding that the technology partner is young and may need some support and guidance.

The idea of changing the culture of the financial services firms was discussed. It was believed that this needed both top-down leadership & funding and also bottom-up drive. An internal innovation fund was set up that enabled small teams working on-the-ground to prepare a business case and pitch over six months to present. Over 70 of these teams took up the challenge, with some generating real business benefits. But, it is more than those end success stories that matter; it is the change in mindset across the company that demonstrates that innovating is part of business-as-usual for everyone in the firm, not just a select few tucked away in an innovation lab.

Other key factors were having both business and IT teams engaged and willing to work together as partners, being able to run projects in an agile (or Agile) fashion and accepting projects that "fail fast", but test and learn quickly. It was interesting to see how business architecture could help in these situations by mapping commonalities across the business using capability models and describing roadmaps aligned to customer journeys.

Practical business design

Source: Practical Business Design

One of the major blockers to building an innovation culture was the procurement process in many large financial services firms. These bureaucratic processes can take over eight months to allow a start-up to being implementing a solution, which can destroy the innovation impetus. A fast-track procurement process, enabling implementation of new technologies, perhaps in some protected sandbox environment, taking eight weeks would be a massive enabler. It feels like there is work required to develop streamlined procurement processes, specifically for innovation technologies.

An audience member asked how many start-ups typically fail. In any typical innovation portfolio, an angel investor may have invested in ten start-up companies. Five of these will likely go bust. Three may remain as the "living dead", where they plod along, just existing as a private company, without any hope of getting a return on the investment. The other two may become "superstars", where they go public with a bang and these two pay off the investment in the other eight start-ups.

I believe that, with more help in providing a consistent analysis of these start-ups on behalf of private equity firms and venture capitalists, the ratio of failures:living dead:superstars could be improved.

This was a very interesting panel discussion and my thanks go to Societe Generale for running the event, the guest speakers on the panel & presenting and to Susanne Chishti for hosting. The themes of technological innovations and the challenges of implementing them in financial services were very familiar to what I have seen in my own experience, but these challenges are not insurmountable with the right support.

If you don't use these new innovations in your business, for example in the field of anti-financial crime, where do you think the criminals are going to go when your competitors
do use them?

Final thought: You cannot wait for the perfect innovation. By the time that happens, your competition may be far ahead of you. You would be better off using what innovation can offer now, but work together with the technology companies to complete that picture for your business.

The right partner can help intersect the old world with the new.

#innovation #event #socgen #data #technology #startup #scaleup #financialservices #ai #ml #rpa #robotics #blockchain #bigdata #cloud #fintech #regtech #legaltech #wealthtech #insuretech #implementingnewtechnologies #leadingpointfinancialmarkets #leadingpointfm #lpfm


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Head of Anti-Financial Crime Design Authority @ Tier 1 Investment Bank


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How will the FCA business plan impact organisations over the next two years?

Leading Point of View
How will the FCA business plan impact organisations over the next two years?

Introduction

The FCA has recently issued its business plan (1) and focus for the upcoming four quarters. Kicking off with some stats – a mix of sobering and positive, the paper gives a clear outline of its proposed, cross-sector, regulatory oversight. One of the greatest challenges for the industry at present is the implementation of MiFID II provisions.
The FCA makes the point that this will facilitate the introduction of ‘major reforms to improve resilience and strengthen integrity and competition in wholesale markets’. Furthermore, work around market abuse will be enhanced. We highlight notable elements of the business plan and their implications for organisations, below.

Cybersecurity

Across all financial sectors lies the risk of cyber-attacks. With the impending implementation and governance of the General Data Protection Regulation, and potential fines of up to 4% of company revenue, organisations’ technological and operational resilience must be second to none. The FCA deems these qualities pivotal pieces of the cyber security jigsaw; it aims to police cyber capabilities and monitor financial crime and all major outages
during the upcoming year.

Senior Managers and Certification Regime

Whilst 2015/2016 saw banks and insurers bring about the operational changes borne out of SMCR, during 2017/2018, the FCA plans to oversee the resulting culture and governance of this significant shift in responsibility. Currently under consultation is the extension, to be implemented by 2018, of SMCR to all firms covered by FSMA. This would cement the prevailing accountability of senior managers’ individual areas of business within the industry.

Customer Engagement & Competition

The theme driving the most recent directives and regulations is placing the ball in the customers’ court. The dramatically changing financial landscape is being molded by the General Data Protection Regulation, the Payment Services Directive 2, to name but a few. The Open API world further allows the customer to have greater choice and engagement with their banking decisions. The FCA is likely to zero in on firms’ development in digitisation and automation and stewardship of customer data with a critical eye, to ensure there is no abuse.

Buy-side | Asset Management

MiFID II implications are beginning to take shape, however there is much to be done. The FCA recognises MiFID II as post-crisis regulation; it is driving reforms that will promote cross-sector market integrity and competition,
and consumer protection. Firms’ annual budgets will now, more than ever, be targeted towards improving IT systems and infrastructure, develop data capabilities, and ensure operational risk is kept at bay.

 

Leading Point Financial Markets brings compelling value at the intersection of Data, Governance & Compliance, and Digital and Operating Model Change initiatives. If you would like to further consider any of these impacts on your organisation, please contact saskia.blake@leadingptconsulting.com or rajen.madan@leadingptconsulting.com.

(1) https://www.fca.org.uk/publications/corporate-documents/our-business-plan-2017-18

 


Top 10 Moments in Financial Markets 2017 – Leading Point


Rules of Data

On 24 October, it was reported that the Financial Conduct Authority launched an investigation into the US credit checking company Equifax; almost 700,000 Britons had their personal data misappropriated between mid-May and July this year. The FCA gave evidence on this matter to the Treasury Select Committee on 31 October because of the significant public interest. The FCA has the power to fine Equifax, or strip it of its right to operate in the UK, if it is found to have been negligent with its customers’ data. With European Union governments formally stating that cyber-attacks can be an ‘act of war,’ data protection cannot be taken seriously enough. The Equifax data breach is by no means a solitary data breach – several large organisations such as Dun & Bradstreet, Verifone, Whole Foods, Deloitte, DocuSign, Yahoo! are already part of the mix.

The Government is aligning domestic data legislation with the European Union in an effort at continuity, despite our plans to leave the EU. The Data Protection Bill, is proof that the Government seeks to keep the UK au courant with the newest data law of EU provenance.

The number of internet users is now close to 4 billion. Businesses continue to move their products and services online in order to service their customers. Data continues to grow exponentially and will persist in its travel far and wide – enabled by technology proliferation. The EU’s General Data Protection Regulation (‘GDPR’) has been precipitated by acute necessity. Companies need to review and revise their approach to privacy, security and governance of their data. A holistic, data protection framework is needed that is centred on the customer and encompasses their interactions, experience, sentiment, along with those of advocacy groups, shareholders, and regulators. This is a non trivial exercise and requires interventions at the mindset, policy, information governance & security and process levels, along with enabling technology.

Businesses are heading in the right direction with GDPR, but there is still a long way to go. Implementing this change with the right spirit is fundamental to building trust with customers and partners. Leading Point’s experience helping organisations with these requirements suggests that while significant compliance hurdles exist, a risk-based approach that focuses on five core areas, will be instrumental to success.

1. Give your customers control over their data – a mindset change

Bearing in mind the territorial scope of the GDPR – across the current 28 EU member states, plus, anyone dealing with the EU, most teams within organisations will benefit from the ethos behind the Regulation. A mindset shift from owning your customers’ data to stewarding your customers’ data is required. Give your customers control over their data. Any legal or natural person processing data must believe in the spirit of this sea change – the need
to assume responsibility for stewarding your customers’ data and to provide them with confidence in your processes. GDPR expands on the list of ‘rights’ each data subject is afforded: the right to be informed, the right to
access data records, the right to data erasure, to name a few. Tone at the top matters immensely.

2. Achieve Data Protection by Design

Which department is leading your organisation’s GDPR compliance efforts? A cross-functional team will help in deploying a holistic data protection framework. To start with, the focus must be on classification of the data, its
supply chain and its governance. Therefore, leveraging existing data management initiatives to embed data privacy requirements can really help in ‘data protection by design’. In practical terms, companies need a clear picture on: ‘what types of data do they hold on their customers;’ ‘which types of data is sensitive and requires enhanced security levels;’ ‘who has access to customers’ sensitive data;’ ‘where is this data processed and distributed;’ ‘how does it flow;’ ‘what is its quality;’ and ‘are their checks and controls in place around its flow and access’? The rules are more stringent now, as companies establish the depth of customer data – their interactions, experiences, sentiments – what impressions are left in an organisation’s data stores. The definition of personal data and its inherent breadth has been redefined – ‘Personal data shall be adequate, relevant and not excessive in relation to the purpose or purposes for which they are processed.’ And so the notion of data minimisation is born. We believe that while there are increasing numbers of quick-fix GDPR solutions in the market, achieving data protection goals is less about technology, and more about energising the organisation into becoming 100% data aware.
Building trust in your data will allow for effective process and controls for data protection, security and governance.

3. The Art of the Process

Focus must be on the ‘process’ exercise – visibility of customer journeys – which processes interact with customer data and the ensuing data lifecycle. Knowing which functions have client-facing processes and ensuring these are
adapted is called for. Threading through specific processes for data collection, data storage, data sharing, access requests and breaches is the focus. Having a command of what happens to personal data, who is involved in gathering it, and responding to Subject Access Requests is important, not least because you will have only a month to respond and cannot routinely charge the current £10. What steps to take in the event of a data breach, how to manage contracts which hold personal data: these are all explicit in the Regulation. For all data processors, we must double down on education and training – on policies, on data governance, on processes and new rules of data. This means highlighting a consistent approach to the different scenarios. Surely the best protection is a body of staff that is wholly informed?

4. Integrating data protection with a risk-based approach

By taking an inventory of obligations to customers via existing contracts and business agreements, organisations can start to manage their stated responsibilities linked to customer data and its management and use. This is a
quick-win.

Data classification and governance exercises will highlight the sensitivity, breadth and depth of data, the access and use of the data held. Data flow will highlight the data processors and third-parties and internal functions involved. Data quality will highlight where data management controls are required to be shored up. In turn, this will flag up priority remediation exercises on customer data.

The aforementioned ‘process’ exercise will highlight key customer-facing process changes, or a requirement to deploy specific data processes referenced by GDPR. Organisations can road-test these processes against the required process turn-around times. For example, data breaches must be reported within 72 hours, and as mentioned above, data subject access requests – one month. Involve your customer services team actively with data protection and security breach scenarios – this will build memory and promote mindset change.

The overarching governance in an organisation will be a key cog in the data protection ecosystem; the Regulation has duly led to the genesis of the Data Protection Officer. Enabling these responsibilities with existing data management governance responsibilities, and appointing data champions, can be an effective approach. Data protection is indisputably everyone’s responsibility, so the emphasis must be on organisational cooperation.

5. Cascading to Third Parties & a Cloud

Third party contracts and the framework that dictates how these are established, must wholeheartedly reflect any changes to the requisite data protection and security obligations. A compliance policy which standardises how third party contracts are established can also be a useful instrument. Data transference should be shored up with model contractual clauses, which allow all parties to clearly realise their responsibilities. We are alive to the persistent risk of cyber attacks, so it is crucial to remember that your data on the cloud is a business issue, as well as an IT issue. Are you fully apprised of where your business stores its data; on the premises, in the cloud, or both? The increasing trend to shift data and infrastructure to a public or private cloud no doubt presents an economic benefit and technology road map for some organisations. But make no mistake, organisations are accountable for their customer data content, its usage, and their security policy for cloud-based storage. Measures such as encryption, pseudonymisation and anonymisation will help, and should be employed as a matter of course, as well as remaining open to select technologies that help underpin cyber defence.

To conclude

When implementing change, evidence-based decision making shouldn’t be the only strategy; knowing which cogs in an organisation interlink cohesively in practice will greatly assist in a robust framework that threads through to
a mindset shift, policy, data, process and third parties. To reinforce an earlier perspective, data is only growing. So are data breaches and cyberattacks. The garnering of our data to feed algorithms and ‘machine learning’, borne
out of the Silicon Valley revolution, is leading to inevitable change in our lives, but we must strive for a democratic jurisdiction for our data. Organisations must give customers control of their data and the confidence in their data
management processes. Rather than penalty-based scaremongering, think of this as an opportunity to build your brand, to send a robust message to your customers and partners, demonstrating care and respect of their data.

To close, a soundbite from the Information Commissioner’s Office: ‘Data protection challenges arise not only from the volume of the data but from the ways in which it is generated, the propensity to find new uses for it, the complexity of the processing and the possibility of unexpected consequences for individuals.’

Leading Point Financial Markets brings compelling value in the intersection of Data, Compliance, Governance and Operating Model Change initiatives.

If you would like to further consider any of the above impacts on your organisation, please contact saskia.blake@leadingptconsulting.com or rajen.madan@leadingptconsulting.com.

 


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