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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Leading Point FM Shortlisted For Data Management Insight Awards

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

 


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

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.

“There is no question that AI shows great promise in the long term – it could transform our industry…” Rob Gruppetta, Head of the Financial Crime Department, FCA, Nov 2018

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?

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

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

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

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

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

 

To improve data quality, firms need to be able to set standards for their internal data and their client’s documentation

 

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

 

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

 

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.

 

A ‘White List’ of transactions allows much smoother processing of transactions compared to due diligence whenever a transaction is flagged

 

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

 

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

 

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

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.

70% of respondents cited ‘Effort. Fatigue & False Positives’ as one of the top 3 biggest challenges for CLM and AFC functions in 2019?

 

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

 

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

 

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

 


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

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.”

 

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.

 

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.

 


LIBOR Transition - Preparation in the Face of Adversity

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

 

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

 

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

“[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)

“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

 

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)

 


Event: Falsely Positive - Is AI the Silver Bullet for Anti-Financial Crime?

ADDRESSING THE AFC BUSINESS CHALLENGES

 

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” “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

 

HOW CAN DATA-DRIVEN AI APPROACHES HELP?

[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

 

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

 

HOW CAN FIRMS GRASP THE AI OPPORTUNITY?

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

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


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


Reducing anti-financial crime risk through op model transformation at a tier 1 investment bank

“Leading Point have proven to be valued partners providing subject matter expertise and transformation delivery with sustained and consistent performance whilst becoming central to the Financial Crime Risk Management Transformation. They have been effective in providing advisory and practical implementation skills with an integrated approach bringing expertise in financial services and GRC (Governance, Risk and Compliance) functional and Fintech/Regtech technology domains."

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


Accelerating growth-at-scale at a treasury blockchain FinTech with our delivery leadership

“Leading Point has been invaluable in helping us deliver high quality client outcomes in the enterprise blockchain space and creating a scalable delivery model for us with increased productivity.”

COO @ FinTech


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.

 


Privacy Preference Center