entity data management handbook 2020 · a-team group chief executive officer angela wilbraham...

Entity Data ManagementHandbook 2020 Sixth Edition

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Entity Data Management Handbook 2020 3

Contents

Editor Sarah Underwood [email protected] A-Team GroupChief Executive Officer Angela Wilbraham [email protected]

President & Chief Content Officer Andrew P. Delaney [email protected]

Editorial Lauren McAughtry [email protected]

Sales - Data Management Insight and RegTech Insight (mid & back office) Jo Webb [email protected]

Sales TradingTech Insight and RegTech Insight (front office) Phil Ansley [email protected]

Marketing Operations Manager Leigh Hill [email protected]

Director of Event Operations Jeri-Anne McKeon [email protected]

Events Content Manager Lorna Van Zyl [email protected]

Group Marketing Manager Claire Snelling [email protected]

Social Media Manager Jamie Icenogle [email protected]

Client Services Manager Ron Wilbraham [email protected]

Production Manager Sharon Wilbraham [email protected]

Project & Events Coordinator Katie Delaney [email protected]

DesignVictoria Wren [email protected] Postal Address Coed Lank Farm Broad Oak Herefordshire HR2 8QY +44-(0)20 8090 2055 [email protected]

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Intro 5Foreword 6Challenges and opportunities 9Entity data 10Hierarchy and ultimate beneficial ownership data 15Use cases 16Data quality 21Entity data management 23LEI 28Regulation 30Outlook 33Glossary 35

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Introduction

High-profile and punitive penalties handed out to large financial institutions over recent years for non-compliance with Anti-Money Laundering (AML) and Know Your Customer (KYC) regulations have catapulted entity data management up the agenda. So, too, have industry and government reports on the staggering sums of money laundered on a global basis.

Less apparent, but equally important, are the ongoing efforts of financial institutions and entity data vendors to challenge these problems by continually improving the quality, accuracy and accessibility of entity data, as well as hierarchy and ultimate beneficial ownership data.

Despite the challenges of sourcing consistent, high-quality entity data, financial institutions are managing it to great effect. Some are using the data to create a 360-view of customers, others to identify business opportunities or improve the customer experience. Common to all is the need to comply with regulations requiring the use of entity data in reporting.

This handbook, the sixth edition of A-Team Group’s ‘must read’ Entity Data Management Handbook, addresses all these issues. It reviews the forthcoming sixth (coincidentally) Anti-Money Laundering Directive that will step up the fight against financial crime by criminalising offences relating to money laundering and the financing of terrorism.

It also delves into the detail of how to achieve entity data quality and tracks development of the Legal Entity Identifier (LEI) designed to standardise entity data, but suffering slow adoption. Moving on, it explores innovative technologies that will help you build an efficient and effective entity data management platform for both regulatory compliance and business development.

Thank you to Bureau van Dijk for sponsoring the handbook. Thank you, too, for your interest in our take on entity data management.

To keep up with the latest developments in this space visit our website at www.a-teaminsight.com.

Angela WilbrahamChief Executive OfficerA-Team Group

How to tackle challenges and gain benefits of entity data management

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6 Entity Data Management Handbook 2020

Foreword

I am delighted to contribute the foreword to the latest edition of the Entity Data Management handbook.

2020 is set to be a fast paced year in enterprise data management. Data management strategy is becoming ‘the’ area where companies can differentiate. Competition to create the perfect data platform is heating up. New technologies are emerging in AI, unlocking the power of unstructured data and aiding graphical representation of information, while new insights are coming to light with alternative data. These developments are helping businesses find that edge, using data to drive growth.

Unique challengesEntity data is critical to data management. The appetite for entity data goes far beyond compliance and KYC. It’s the cornerstone of most master and enterprise data management projects.

No one size fits all when it comes to entity data management. Unique challenges demand unique solutions. But there are common challenges we still see data managers grappling with in entity data management.

Most data management projects start with entity resolution and matching, whether those entities are companies, individuals or other ‘things’. Entity resolution depends on data quality: names and reference data. This drives the initial matching and further data management processes.

Without identifiers there is a huge disparity in consistency. Multiple sources, such as data vendors, exchanges, regulators, rating agencies and LOUs, all with varying standards, formats and structures, are often required to create the ‘golden record’. Technology, especially AI, is becoming increasingly important in this space, allowing both structured and unstructured data to inform the resolution.

Putting you in controlTechnology is driving better approaches to entity data management, putting user choice and experience at their core

Penny Nash is Bureau van Dijk director of product strategy

Contact her at: [email protected] www.bvdinfo.com

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Foreword

Data qualityData quality remains the hardest nut to crack, especially with a multi-source approach. There is the choice of data sets to link and the constant quest for the perfect balance between timeliness and accuracy. De-duplication and mastering is the goal data managers are striving for, yet there is no one silver bullet. The golden copy is always your version of the truth based on the number of sources, levels of quality and amount of governance you choose.

The many new alternative and unstructured data sets available still have to be linked to key entity data to make sense. Which brings us back to the age-old challenge of correctly linking and cross-referencing. This requires a plethora of reference data and a unique-ID strategy that is robust, yet flexible enough that it won’t lead you to depend entirely on one provider.

Putting users centre stageData and technology providers continue to provide an array of solutions from which firms can pick and choose. That flexibility and choice is becoming ever more important.

At a minimum, data buyers and managers expect a high standard of quality data for entity resolution, mapping, linking and integration. Strong reference data is critical in such data management projects to give the maximum choice of identifiers and strong unique IDs, the glue in the project. Organisations such as GLEIF (the Global Legal Entity Identifier Foundation) are helping everyone take a positive direction with standardisation of reference data.

Against this backdrop, data managers are going to expect more. The differentiators are likely to be flexibility and automation, leading to a leaner, self-serve customer experience.

Imagine you could explore, visualise, analyse, connect, derive and distribute any or all of your data in one environment. This year, the technology is here to make that possible. AI technologies are strengthening entity-resolution and news-gathering processes. And, along with good data governance, strong relationships between clients and



Foreword

solution providers will significantly help data managers to realise their goals, from simplification to cost cutting, revenue generation through data, MDM and compliance efficiency.

The perfect platformPutting the end user in full control is the key to improving the data management experience. In the end, data managers must decide on their single view, or ‘truth’. The addition of non-traditional data sets means users need flexible and easy ways to weave data together as they pick and choose from what’s on offer.

2020 is certain to see this shift towards better delivery and a more seamless data management user experience. Businesses are looking for a flexible, scalable and fully integrated data solution for optimised data management and distribution across all data channels. What will set the best data market players apart is a one-stop shop, enterprise-grade data management platform rather than multiple data solutions requiring integration.

At Bureau van Dijk, we’ve captured, treated and standardised data for more than 30 years, working with unique IDs and strong reference data to link data from more than 165 providers and a huge number of our own sources today. We face the same challenges as all data managers. This year we will continue to work with cloud and AI technologies to provide more advanced data management solutions to make it even easier for our customers to get the best of the breadth and depth of entity data we offer, helping them to make better, faster decisions.



Challenges and opportunities

ChallengesThe challenges of getting entity data right are nowhere clearer to be seen than in statistics related to Anti-Money Laundering (AML). According to the United Nations Office on Drugs and Crime (UNODC), the estimated amount of money laundered globally in any one year is between 2% and 5% of global GDP, or $800 billion to $2 trillion.

Looking at the problem slightly differently, research by Encompass Corporation shows 58 AML related penalties totalling $8.1 billion were imposed globally in 2019, double the amount and towards double the value of 2018 figures showing 29 penalties totalling $4.3 billion. However you cut financial crime, the sums of laundered money are staggering and in danger of spiralling out of control, potentially causing economic damage to local jurisdictions and the global economy as a whole.

There are, and always will be, bad actors intent on committing financial crime. The challenge for financial institutions is to continuously improve the quality, completeness and timeliness of entity data, hierarchy and ultimate beneficial ownership data, and ensure effective entity data management.

OpportunitiesFinancial institutions with access to high quality, accurate and timely entity data can not only avoid the penalties of non-compliance with AML, but also use the data to their advantage. Strong entity data delivers satisfied customers and the possibilities of extending relationships with customers and building reputation to acquire more. Analysis of customer data can provide business insight into opportunities in both product development and adjacent markets.

With access to hierarchy and ultimate beneficial ownership data, firms can understand company relationships and better identify promising customers and those it would be better not to do business with, in turn improving risk management.

The data can also be used by financial institutions to gain a 360-view of customers and track mergers and acquisitions, while linking the data to other data sets can create an entity-centric view across an organisation. Equally valuable are efforts to standardise entity data using Legal Entity Identifiers (LEIs) and deliver greater efficiency, transparency and accuracy across capital markets.



Entity Data

The criticality of entity data became apparent during the 2008 financial crisis, when neither financial institutions nor regulators had access to the data they needed to identify parties to transactions and aggregate counterparty risk exposure in a timely and meaningful manner. When Lehman Brothers collapsed in September 2008 and the aftershock ripped through financial markets, the writing was on the wall – entity data is central to the save and smooth operation of global capital markets.

While the Lehman Brothers disaster brought entity data into sharp focus as a means of identifying clients, counterparties and issuers of securities, its use cases have extended beyond regulation and into business, bringing with them issues around entity relationships, entity data hierarchies, data quality and management.

The use of accurate, complete and timely entity data can be the difference between regulatory compliance and penalties for non-compliance, doing business with the right or wrong customers, and staying in line with sanctions and Politically Exposed Persons (PEPs) requirements or breaching these and incurring eye-watering financial fines and reputational damage.

Regulatory regimesWith so much at stake, entity data is key to regulatory regimes such as Know Your Customer (KYC) and AML, both of which have become immensely important to both financial markets and the economy at large, as bad actors are still slipping through KYC checks, financial crime is escalating, and money laundering continues to rise at an alarming rate.

Other regulations resulting from the financial crisis and relying on entity data to identify clients, counterparties and issuers include Dodd-Frank, European Market Infrastructure Regulation (EMIR), Markets in Financial Instruments Directive II (MiFID II) and Markets in Financial Instruments Regulation (MiFIR). More recent additions include AnaCredit, which was finalised in 2016 and started data collection iin 2018; and Securities Financing Transactions Regulation (SFTR), which takes effect in April 2020. (Read more about regulation and entity data on page 30)

Legal Entity IdentifierThe financial crisis also led the G20 and European Union to call for the creation of a global Legal Entity Identifier (LEI) system to provide standard LEI data on a global scale that could uniquely identify parties to transactions, and provide a



Entity Data

foundation on which to improve risk management. (Read more about the LEI on page 28)

Open entity dataOpen entity data and entity data identifiers are available in the market, led by the LEI, but also provided by organisations such as OpenCorporates, which supports a global database of more than 100 million companies and their data, and large data vendors such as Refinitiv.

Refinitiv introduced the Permanent Identifier (PermID) in 2015 and it is widely used both within Refinitiv and among its customers. It is a machine-readable identifier developed to create a unique reference for identification across a wide variety of entity types including organisations, instruments, funds, issuers and people.

Adding valueAs entity data has become essential to fighting financial crime and key to business issues such as gaining a 360-view of customers, there is often a need to add value to the data. While the LEI provides a database of unique entity identifiers that can be used to cross-reference other vendor and proprietary identifiers, and hierarchy and beneficial ownership data that can provide essential knowledge about

corporate relationships, more and different data sets are needed to complete an entity-centric view across an enterprise.

These data sets could include valuations and evaluated pricing, fundamental data, ratings data and credit analytics, all adding value to the LEI and drawing a fuller picture of each entity a company does business with. While the LEI has no means of linking directly to added value data sets, mapping it to other vendor and proprietary entity identifiers with links to the data sets can provide the building blocks for an entity-centric view of enterprise activity and a step up in risk management, customer service and operational efficiency.

Increasing interest in the use of unstructured and alternative data also has the potential to add value to entity data in a way that has not previously been possible.

Sourcing entity dataWhile LEI data is freely available and the fifth Anti-Money Laundering Directive (AMLD5), which came into law in January 2020, increased access to entity data by interconnecting EU member states’ central beneficial ownership registers and enhancing access to them, sourcing entity, hierarchy and ultimate beneficial ownership data

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Entity Data

can be more difficult where private companies are concerned. Unlike publicly listed companies, private companies are not required to disclose significant investor holdings.

Some of this data resides within financial institutions, but the challenge of creating different entity data sets required by different regulations, and the need to avoid the costly penalties of non-compliance, mean many firms look to specialist external providers to source hierarchy and ultimate beneficial ownership data of private companies.

This can be complex and inconsistent, with regulations defining beneficial ownership at different thresholds. By way of example, AMLD5 and US rules set down by the Financial Crimes Enforcement Network (FinCEN), define beneficial ownership as ownership of 25% or more of a legal entity. In contrast, the US Foreign Account Tax Compliance Act (FATCA) pitches beneficial ownership at a 10% or larger holding in an entity.

At the extreme, and in highly sensitive or volatile situations, companies may need access to ownership data at thresholds in single figure percentages, a requirement that can be met by specialist hierarchy and beneficial

ownership data providers with teams of researchers dedicated to the search for securities holdings however large or small.

Entity data suppliersFinancial institutions source entity data from internal sources as well as suppliers including market data vendors, niche providers dedicated to entity data, business registries and LEI issuers within the global LEI system.

Whatever their size and scope, the role of entity data suppliers in highly regulated markets is to continually improve the granularity, accuracy, consistency, timeliness and quality of data they provide to support regulatory compliance programmes, business initiatives and efforts to cut down financial crime.

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Entity Data

To create an entity record, vendor research teams typically collect entity data from primary sources, including registration documents, regulatory filings, exchange announcements, annual reports and prospectuses. The data is verified and automatically cleansed to identify and correct any inaccurate or incomplete data, and to eradicate typographical errors.

The data is loaded into an entity database and constantly monitored and updated when it is affected by corporate actions or other events to ensure provision of complete and accurate data. It is also linked back to primary sources to provide an audit trail that helps firms meet regulatory transparency requirements, including the ability to show exactly how entity data has been sourced and changed.

Depending on customer requirements, data vendors can map LEIs, proprietary entity identifiers and vendor identifiers, reconcile underlying data and deliver an entity data master that provides a single, accurate and consistent view of entities. They can also automate and aggregate data to create data sets required for regulatory reporting, and supply entity data hierarchies including parent and ultimate beneficial ownership information that can be used in risk

management systems to provide a better understanding of company relationships and risk exposure.

From a financial institution’s perspective, integrating entity data from multiple sources can be essential to gaining a single view of clients, prospective clients, counterparties and issuers, increasing efficiency and providing resources to make better business decisions.

Access and distributionThere are several options here, many based in the cloud to provide flexibility, scale and the benefits of off-premise systems. Managed services are a case in point and often use application programming interfaces (APIs) to allow users to pull required entity data directly into in-house or third-party applications and data management systems.

Multi-tenanted entity data utilities use a one-to-many model that allows users to access data that is processed once for the benefit of many utility users. The aim is to cut the cost of cleansing, validating and distributing common data that is used by most firms.

Other options include vendor provision of online portals, entity data feeds, and flat data files of requested entity data.



Hierarchy & ultimate beneficial ownership data

Level 2 data includes: • Direct accounting consolidating

parent• Ultimate accounting

consolidating parent.

Legal entities that have or acquire an LEI must report this data, which provides relationship data to answer the question of ‘who owns whom’. It allows identification of the direct and ultimate parents of a legal entity and vice versa, so that entities owned by individual companies or the parents of entities can be researched.

More detailed, and often difficult to find, hierarchy and beneficial ownership data related to private companies is sourced, validated and provided by specialist data vendors dedicated to entity data. They maintain data on millions of legal entities and their hierarchy and beneficial ownership data, and overlay the data with lists of sanctions, PEPs and adverse media. This provides a clear and complete picture of each entity and its relationships, and supports a full understanding of risk exposure, and the ability to identify potentially bad actors.

The LEI can provide a starting point for entity data management programmes, be they regulatory compliance projects, risk management improvement schemes, or customer experience enhancement initiatives.

However, the LEI alone is not enough to build complete solutions. While the identifier offers the ability to identify clients, counterparties and issuers of securities, GLEIF also provides hierarchy and ultimate beneficial ownership data to provide a better understanding of business entities and their relationships with other entities.

Level 1 reference data that must be supplied for each LEI includes business card and other information that provides the answer to the question of ‘who is who’.

Level 1 date comprises: • The official name of the legal

entity as recorded in official registers

• The registered address of the legal entity

• The country of formation• The codes for the representation

of names of countries and their subdivisions

• The date of the first LEI assignment, the date of the last update of the LEI information, and the date of expiry, if applicable.



Increasing enforcement of KYC, AML and sanctions rules and regulations, coupled to potentially large fines for non-compliance and reputational damage, highlight the need to get these use cases of entity data spot on. Less high-profile in the media, but equally important to financial institutions, are the use of entity data to drive business development and improve enterprise risk management.

While these use cases have different objectives, they also have much in common, including the need for accurate, complete and timely entity data that is continually maintained, updated and can be used to form a precise audit trail.

KYC and client onboardingKYC includes client onboarding, due diligence, enhanced due diligence and ongoing entity data verification with the aim of preventing activities such as money laundering, financial fraud, identity theft and terrorist financing. Essentially, client onboarding is the process financial institutions must complete before doing business with a client. It is an extensive process based on entity data and must comply with local KYC regulations. Historically, it was a manual data input process, but that is no longer feasible for

large organisations onboarding many clients, working in highly competitive markets, and subject to the scrutiny of local supervisory authorities.

Automation, often including machine learning and AI, is critical to the efficiency and efficacy of client onboarding, and to delivering business benefits such as reduced time to revenue, client loyalty, improved client experience, identification of new products and matching them to client segments, indicating potentially ‘good’ and ‘bad’ customers, and reducing costs.

A handful of large financial institutions have in-house client onboarding applications, but most, for reasons of cost, time, lack of human resources and potential operational risk, have adopted vendor solutions. These tackle the data management challenges of client onboarding and due diligence, as well as client lifecycle management. They also solve problems of client and bank dissatisfaction with existing systems by automating processes to the greatest extent possible, ensuring ongoing data maintenance, updating data throughout the client lifecycle, off-boarding clients, and providing an audit trail that meets regulatory requirements.

Use Cases



Use Cases

Vendor solutions typically use a rules-driven, risk-based approach to customer onboarding and KYC compliance based on a central data repository and focussing on higher risk clients.

Anti-Money LaunderingAML is closely coupled to KYC, and is an EU directive designed to prevent use of the global financial system for money laundering and counter terrorist financing (CTF). AMLD5 improves transparency of beneficial owners of legal entities, trusts and similar legal arrangements. This is done by making EU member states’ beneficial ownership registers of legal entities publicly accessible, and making registries of trusts and similar legal arrangements accessible to competent authorities, national Financial Information Units (FIUs), obliged entities in the context of their due diligence measures, and any person who can demonstrate a legitimate interest.

Where beneficial ownership registers are in place, KYC processes must consult them before new business relationships are made. This provides an additional level of security against bad actors, but also extends data management requirements to achieve compliance.

Following on the heels of AMLD5, AMLD6 must be transposed into law across all EU countries by December 2020, and regulated entities within the region will have until June 2021 to implement the directive.

Sanctions screening Client screening applications check entity data against financial sanctions, trade embargoes, PEP lists and other watch lists to detect whether clients are suitable to do business with and, if this is not clear, raise an alert for additional checks to be made by application users.

Typically, screening and sanctions solutions are automated to match entities and sanctions, with exceptions handled manually and solution vendors working to reduce false positive outputs that detract from the focus on real sanctions issues. Vendors are also introducing machine learning and AI with a view to improving early detection of entities that are sanctioned.

Sanctions lists are made and maintained by organisations such as the US Office of Foreign Assets Control (OFAC), the United Nations (UN) and EU member states working in line with sanctions made through EU law. The lists change continually and grow rapidly, requiring screening applications to monitor



master data management solution, financial institutions can gain a 360-view of customers, giving them a clear understanding of who they are doing business with, how customers are behaving, and how best to retain clients and acquire more in adjacent sectors. They can also use the data to identify new business opportunities either with existing or prospective customers.

Data enrichment services provided by entity data vendors can extend entity records with content such as detailed financials, financial strength data and other risk metrics, extensive corporate structures, and B2B sales and marketing intelligence, again providing business with the power to make better decisions and identify new opportunities.

Customer experience is vastly improved by automated, centralised systems that hold relevant and permissioned data on each customer that can be reused as necessary, perhaps when a customer wants to buy an additional product. This obviates the need for financial institutions to reproduce the onboarding process every time there is a change in relationship with a customer.

them closely to identify sanctioned individuals, organisations, and countries, and avoid too many false positives.

Explicit sanctions covering countries, individuals or entities are relatively easy to manage and enforce, but complexity is introduced when entities are sanctioned by extension. Under OFAC’s 50% rule, a company is sanctioned by extension if owned by a sanctioned company or individual through a chain of ownership of 50% or more. These entities do not appear on any sanctions lists, but you could still be fined for trading with them.

Business developmentEntity data, managed well, is a prime driver of improved customer experience, acquisition, engagement, retention, and by extension, business development. By implementing an entity data

Use Cases



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Use Cases

Risk managementThe criticality of accurate legal entity date for risk management is nowhere more apparent than in the outcome of the 2008 financial crisis that brought down financial institutions as a result of lack of understanding of counterparty exposure and inability to aggregate risk caused by poor and incomplete entity data.

Regulations, including Dodd-Frank, EMIR and MiFID II, redress this problem by extending data management and reporting rules – in many cases insisting that the LEI is used to uniquely identify entities, mandating tighter controls, and calling for improved transparency across markets.

Firms are also working to improve data quality and linking entity and instrument data to understand exposure by industry, issuer and region. With accurate and complete entity data, and a good understanding of workflows and applications, they can also benefit from improved operational risk management including a reduction in reconciliations and trade failures.

Capital adequacyAs well as using entity data to ensure regulatory compliance, financial institutions are investing resources in improving risk management

applications. By increasing the accuracy of identification in risk aggregation and categorisation of exposures, they can reduce the levels of regulatory capital they must hold under Solvency II, Basel III, and forthcoming Fundamental Review of the Trading Book (FRTB) capital adequacy rules.



Data Quality

High quality entity data is imperative to all the data’s uses cases, but it is not easy to achieve and sustain without the help of tools designed specifically for the purpose.

Most entity data quality issues result from data silos and legacy systems storing and using conflicting data sets, manual data collection and management processes, and inconsistencies in entity data feeds from data vendors. These problems cause data duplication, version issues, incorrect records and incomplete data sets that can lead to operational inefficiencies, poor customer service, risk exposure, and inaccurate regulatory reporting.

With so much at stake, financial institutions are establishing entity data quality programmes and implementing vendor tools and services. An initial approach to improving entity data quality should start small, identify high-risk data and ensure its accuracy is sustained. The initiative can then be cascaded to cover additional levels of entity data and its associated risk.

One practical approach to improving data quality, particularly where data is spread across silos, is to implement a master data management solution designed to manage and deliver golden copy entity data.

Other solutions include fuzzy matching, that can identify duplicate records and merge data to create single records, and entity data diagnostics that identify and prioritise data quality issues and can be used to establish a baseline and monitoring schedule to ensure data quality is maintained at an acceptable level. Data quality metrics provide constant measures across data sets and can be used to support a continuous iteration of measurement, decision making and data remediation.

As in every aspect of financial data management, machine learning and AI are beginning to offer support for data quality. Natural Language Processing (NLP) is also emerging as a means of identifying entity data in any kind of text, and extracting and cleansing the data.



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Entity Data Management

Entity data management is crucial to delivering regulatory compliance, improved client onboarding and KYC, effective AML, sanctions screening, a robust understanding of risk exposure, and the potential for business development.

Regulation was the early driver of entity data management as firms responded to requirements to identify all parties to financial transactions, aggregate risk exposure to counterparties quickly and accurately, and exclude sanctioned entities and companies from financial transactions. While these requirements remain, many firms are driving better business processes and opportunities out of entity data management.

With many entity identifiers including the LEI being used by financial institutions, a master data management approach can source and integrate LEIs, proprietary identifiers and vendor identifiers from multiple systems, map them to each other and reconcile underlying data.

When this is done, it is possible to see real benefits. Regulatory requirements for entity data can be met accurately and in a timely manner, a single version of entity data can be delivered to diverse downstream applications ensuring integrity of the data, the data can

be enriched by building up entity hierarchies and relationships to discover ultimate beneficial owners, and a 360-view of customer data can be achieved.

A strategic approachA strategic approach to entity data management backed by strong data governance and lineage can provide firms with robust risk management, a deep understanding of entity relationships, and a platform that can not only answer questions from regulators, but also offer opportunities for cross-selling and new product development.

It can also be rolled out across an organisation, ironing out departmental differences around entity data, demonstrating the benefits of effective data management, and helping to change an organisation’s culture to one that understands and enforces the importance of consistent, high quality entity data.

How to build an entity data management platformThe challenges of building efficient and effective entity data management platforms are many and at a fundamental level include identifying entity data, pulling it together from multiple data silos, filling data gaps, and integrating internal and external data sources




to create an entity data master. The data must be formatted, validated and given an identifier. It also needs to be of high quality, accessible and well maintained, allowing the entity data master to provide a single, consistent and timely view of all entities with which a financial institution is involved.

An entity data master can also help firms address operational problems such as stale and out-of-date data that is difficult to monitor and update when held in multiple silos. And it can lower the cost of owning and managing customer and other entity data.

Initially, most banks developed entity data management platforms in house, but the sheer volume of data that must be handled for regulatory purposes, such as KYC and AML, means many have migrated to vendor applications and services, or developed a hybrid build-and-buy solution. These solutions often cover specific elements of entity data management, perhaps sanctions screening or client onboarding, although there are also complete client lifecycle management platforms.

Approaches to operational entity data management vary. While the LEI has not yet gained as much traction

as initially expected, mostly due to a lack of regulatory mandates, it serves well as a cross reference point for multiple proprietary and vendor entity identifiers, links entity data sets within an organisation, and is sometimes, but not always, mandated for use in transactions between companies and for regulatory reporting.

Data management solutions While the end game of entity data management is provision of a single, accurate, consistent and frequently updated view of every entity with which a financial institution has relationships and does business, there are a number of ways to get there. Depending on existing data management architecture and appetite for change, firms may choose to build entity data management solutions in house or opt for vendor solutions that fit their business models. Vendor solutions include enterprise software, hosted solutions, managed services, data utilities, and web services:

Enterprise software: Vendor software solutions deployed in house by financial institutions to manage entity data, typically taking a master data management approach that centralises client, counterparty and issuer data.

Expected improvements from




implementing enterprise software include greater operational efficiency, reduced cost and enhanced data quality and understanding.

Hosted solutions: Firms with limited IT resources or strategies to outsource non-core activities can benefit from hosted solutions that remove the burden of deploying and maintaining software in house and provide access to entity data management software that is hosted – often in the cloud, and maintained by a vendor. These solutions are based on a vendor’s enterprise software and provide a dedicated instance of the software for each client, as well as the ability to configure the software to meet business needs.

Expected benefits include reduced total cost of ownership, increased speed to implementation, enhanced data quality and a lower skills requirement.

Managed services: Like hosted solutions, managed services are often hosted in the cloud and remove the burden of deploying and maintaining software in house, but unlike hosted solutions they are based on a central vendor managed platform that is used by many clients. Typically, a managed entity data management service will cleanse, deduplicate and validate entity data, and map

vendor and industry standard entity identifiers to the client’s identifier to deliver an entity master.

Expected benefits include reduced total cost of ownership, increased speed to implementation, flexibility to meet changing requirements and enhanced data quality.

Data utilities: The aim of utilities is to ease the industry’s data management burden and deliver economies of scale by consolidating non-competitive data management processes that are repeated across financial institutions.

Expected benefits of utilities match those of other managed services, but whether they can go further in terms of reducing costs, improving data quality and enhancing operational efficiency has yet to be confirmed.

Web services: Web services provide API access to vendor entity data that



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has been cleansed and validated. These services can be used to match and validate entity data, populate master data management solutions, monitor and refresh data, and flow entity data directly into applications and systems.

Expected improvements of web services include reduced costs, increased speed to implementation, flexibility to meet changing requirements, enhanced data quality, and the ability to seamlessly integrate entity data with applications and systems.

TechnologiesThe criticality of entity data management has led many financial institutions to review existing systems and processes, and consider innovative technologies that can provide greater operational efficiency, faster time to market, reduced costs, improved data quality and better customer service. These technologies include automation, machine learning, cloud, semantics and blockchain:

Automation: The automation of entity data management is essential to ensuring regulatory compliance and efficiency in a highly competitive market. Some organisations develop their own automation processes, but more opt for vendor cloud-based software-

as-a-service solutions offering a combination of near real-time data management, robotic process automation and cognitive learning.

Both client onboarding and corporate actions can benefit from automation, with onboarding including entity data gathering, document checking, hierarchy building, reconciliation and other previously manual processes. Automation of corporate actions can monitor announcements such as dividends, mergers and acquisitions, and extract attributions relevant to entity data.

The benefits of process automation include time to market, improved customer service, reduced risk and operational cost, more confident regulatory compliance, data quality and transparency.

Machine learning: Machine learning is a branch of AI that gives systems the ability to learn automatically and improve from experience without being explicitly programmed and either with or without human intervention. Learning is based on algorithms that can, by way of example, detect patterns in data or apply rules to predict likely outcomes based on identified patterns, categorise data, identify previously unknown patterns and relationships, and detect unexpected behaviour.




Applied to applications using entity data, machine learning can speed up processes, increase accuracy and efficiency, highlight exceptions and help firms find new business opportunities.

Cloud: Adoption of private, public and hybrid cloud solutions for financial services applications includes the move of entity data management into the cloud. Cloud solutions offer the ability to scale in line with data volumes and run test or pilot projects without the need for capital investment.

Cloud technology offers the benefits of cost savings, rapid deployment, ability to scale, flexibility to meet changing requirements, mobility and, in many cases, improved security.

Semantics: Semantic technology is being deployed in financial institutions to answer complex questions quickly and efficiently. While a simple keyword search finds data, a semantic search analyses the structure of a question to determine its context and meaning, and returns a relevant answer.

Applied to entity data, one use case of semantics is a 360-view of customer information. Semantics provide the ability to store data about a customer as the data is discovered and ingested from many

diverse sources, without having to go through long and costly extract, transfer and load (ETL) cycles. It is particularly useful when data is non-relational, customer onboarding documents stand out here, and therefore difficult to store in a relational database.

By employing semantics, firms should be able to make more meaningful internal data searches and improve the customer experience by providing better and faster responses to complex enquiries.

Blockchain and distributed ledger technologies: Initial use cases of these technologies in financial services were in post-trade transaction and payments processing, both of which include entity data. More recently, KYC applications have integrated blockchain or distributed ledger technology to benefit from the ability to share customer information between users on the basis that the technology enforces ‘single source of truth’ data sets and immutable transaction logs.

While these are early days for blockchain and disturbed ledger technologies, they can offer basic advantages of decentralisation, immutability, security, and transparency.



Legal Entity Identifier

The LEI is a free-to-use standard entity identifier that uniquely identifies parties to financial transactions. The first issuance of LEIs was made in 2013. Initial adoption was slow, but picked up when MiFID II went live with a mandate in July 2018 to include LEIs in reporting. The number of LEIs issued has since continued to rise, but needs to rise further to make the identifier prevalent in capital markets. The number of LEIs issued at the time of writing this handbook was 1.6 million, up from 1.4 million in June 2019.

While there are mixed opinions on the success of the LEI, more regulatory mandates requiring the use of the identifier would drive larger-scale adoption. To date, the LEI is required predominantly by European regulations MiFID II, EMIR, Solvency II, Benchmark Regulation, SFTR and Market Abuse Regulation (MAR).

It is present in the US through the Dodd-Frank Act and is gaining ground on a global basis – https://www.gleif.org/en/lei-solutions/regulatory-use-of-the-lei. The elephant in the room remains the US Securities and Exchange Commission (SEC), and to a lesser extent other US authorities, that have yet to mandate the use of the LEI. If and when they do make such mandates, the identifier could become a key and consistent element of entity data management and global financial reporting.

A brief historyThe development of the LEI and a global LEI system was mandated by the 2011 G20 Cannes Summit in the wake of the 2008 financial crisis and in the hope of averting further crises. The LEI was designed to help regulators measure and monitor systemic risk by identifying parties to transactions quickly and consistently, and obtain an accurate view of their global exposures.

Based on recommendations made by the FSB following the 2011 G20 mandate, the global LEI system includes three key elements:

The Regulatory Oversight Committee (ROC), which includes regulators from around the world that have agreed to participate in the global LEI system. The ROC



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Legal Entity Identifier

took over management of the global LEI initiative from the FSB in January 2013.

The Global LEI Foundation (GLEIF), which was set up in June 2014 as a Swiss foundation and non-profit organisation with a reporting line to the ROC. It is the operational arm of the global LEI system and is responsible for the uniqueness of LEIs, open and free access to the identifiers, and high quality reference data. It is also responsible for the accreditation and monitoring of local LEI issuers.

The GLEIF website – www.gleif.org, which provides a number of services including information on how to get an LEI, a list of LEI issuers, a searchable global LEI index of all issued LEIs, a daily concatenated file of LEI data, monthly data quality reports and numerous facts, figures and statistics on the global LEI population.

Landmark developmentsThe LEI and related services have been developed significantly since the GLEIF took over operational responsibility of the global LEI system. These include: • A data quality management

programme based on clearly defined criteria that allow the quality of the LEI data pool to be monitored and optimised

• A facility to challenge LEI data that offers an easy and convenient means to trigger verification and correction

• Provision of Level 2 ‘who owns whom’ data that adds to Level 1 ‘who is who’ data

• An LEI look-up API that allows developers to access the LEI data pool directly

• GLEIF certification of the LEI mapping service, a free of charge service designed to ensure organisations mapping the LEI to their own identifiers use consistent processes

• Downloadable BIC-to-LEI and ISIN-to-LEI relationship files

• Work with XBRL Global to include the LEI in XBRL taxonomy

• Golden copy files that respond to market requirements for more frequent publication of LEI data to support different time zones – the files are distributed three times a day at eight-hour intervals and are accompanied by delta files that identify only newly issued LEIs and revisions to an LEI’s reference data

• Beta version of LEI Search 2.0 that supports searches for LEIs, corresponding legal entity reference data and relationship reference data

• GLEIF pioneers the inclusion of LEIs in digital, machine-readable financial documentation.



Regulations requiring entity data

Regulatory reform following the 2008 financial crisis sought to stabilise and secure capital markets by closing information gaps, providing a clear view of counterparty and market risk exposure, and improving transparency across financial markets. These intentions are embedded in a number of implemented and forthcoming regulations that have a focus on entity data, including the LEI.

A snapshot of the entity data and LEI requirements of some of these regulations is provided below. Additional detail can be found in A-Team Group’s Regulatory Data Handbook.

AMLD6

The sixth AML directive will step up the fight against money laundering by introducing criminal law provisions aimed to harmonise criminal offences relating to money laundering and the financing of terrorism across Europe. The directive provides a single definition of money laundering applicable across the EU to improve judicial and police cooperation in different member states. It also sets out a list of offences and sanctions related to money laundering, including 22 categories of the crime. Penalties include a maximum of four years imprisonment for those found guilty. benefits and funding.

As AMLD6 makes signification changes and additions to AMLD5, the time is now to understand all aspects of the regulation, including its entity data requirements, and implement compliance solutions. Depending on the efficacy of the sixth directive, the EU may see fit to introduce a seventh directive or even implement AML regulation that would have a direct effect across member states.

Dodd-Frank

The Dodd-Frank Wall Street Reform and Consumer Protection Act is a US Government regulation that took effect on July 21, 2010. It aims to prevent another financial crisis by creating processes that enforce transparency and accountability across financial markets. It also implements rules for consumer protection.

To achieve its aims, the regulation focuses on standardisation of reference data, calls for the creation of new data, and issues guidelines on reporting formats and how to maintain and analyse data. The requirement for standardised reference data is designed to improve the quality of financial data available to regulators so that better risk analyses can be made.

The LEI facilitates these objectives by consistently identifying parties to




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financial transactions and supporting aggregation of risk information associated with each legal entity.

EMIR

European Market Infrastructure Regulation (EMIR) is an EU regulation that took effect on August 16, 2012. It aims to improve transparency and reduce risk in over-the-counter (OTC) derivatives markets. To achieve this, EMIR requires OTC derivatives to be cleared using a central counterparty (CCP) that is listed in the European Securities and Markets Authority (ESMA) registry and authorised under EMIR.

The regulation also includes risk mitigation procedures for bilaterally cleared OTC derivatives and requires all derivatives transactions to be reported to a trade repository.

Reports must include LEIs and Unique Trade Identifiers (UTIs) to identify parties to transactions and improve transparency. Both parties to a trade must ensure data related to a concluded trade, as well as data related to the entities involved, is reported to a trade repository.

Most recently, EMIR trade reporting has been extended to align with MiFID II reporting templates, which also include requirements for LEIs.

KYC Know Your Customer (KYC) is not a single regulation, but the term used to describe requirements around customer due diligence that are made and enforced in different jurisdictions with different legislative regimes.

KYC covers the process companies must go through to identify and understand customers before conducting financial business with them. It includes onboarding, due diligence and sanctions screening, and must be revisited frequently to ensure customer entity data is up to date, complete and correct throughout the customer lifecycle.

As well as being an element of customer due diligence, the KYC process is part of AML and is included in MiFID II.

MAR

Market Abuse Regulation (MAR) is an EU regulation that came into effect on July 3, 2016. The regulation strengthens EU rules on market integrity and investor protection that were first adopted in the 2003 Market Abuse Directive (MAD). The regulation aims to challenge insider dealing and market manipulation in Europe’s financial markets.

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ensuring harmonised investor protection across Europe.

MiFIR acts as the regulatory reporting arm of MiFID II. Both the directive and regulation require use of the LEI to identify issuers and counterparties, and fulfil regulatory reporting obligations.

SFTR

Securities Financing Transactions Regulation (SFTR) is an EU regulation with a phased reporting requirement starting in April 2020. It is designed to highlight transactions that could pose a significant level of systemic risk and specifically sets out requirements to improve market transparency of securities financing. securities financing transactions (SFTs) are typically transactions that use securities to borrow cash, or vice versa.

The regulation requires two-sided reporting, with both collateral provider and collateral receiver required to report their side of the SFT to an approved trade repository. Reports must use LEIs to identify counterparties and other parties involved in an SFT, such as agent lenders, central counterparties and central securities depositories.



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Many of the provisions of MAR are the same as those in the initial MAD directive, but the regulation extends the scope of previous rules to include new trading platforms and technologies, and commodity and related derivative markets. It also bans the manipulation of benchmarks and introduces the LEI to reinforce the investigative and sanctioning powers of regulators.

MiFID II and MiFIR

MiFID II and Markets in Financial Instruments Regulation (MiFIR) are EU requirements that had an effective date of January 3, 2018. MiFID II extends the remit and scope of its predecessor, the original MiFID introduced in 2007, and aims to improve the competitiveness of European markets by creating a single transparent market for investment services and activities, and



Outlook

Regulation and innovation will continue to drive up the quality of entity data and execution of entity data management for the foreseeable future, but the pace will accelerate as AMLD6 comes into force later this year, criminalising AML and KYC offences, and calling for more attention to the detail of entity, hierarchy and ultimate beneficial ownership data.

This will be delivered by technology innovation, increasing automation, and ongoing implementation of machine learning and AI solutions that can handle the mass of entity data cleansing, validation and matching, reduce false positives in sanctions and screening processes, and raise alerts of potentially damaging data that must be manually managed.

Beyond these kinds of use cases, technologies we know and those coming over the horizon will drive business opportunity. Entity data will be turned into increasingly valuable insight into what customers want, how the business can deliver, and more. While this could bring competitive advantage, the caveat remains that gains will only be made with a focus on getting the fundamentals of entity data management right and looking ahead to develop more ambitious strategies.

Growing interest in digital identity and identity certification will also rely on accurate entity data. GLEIF, by way of example, has started to consider the benefits of digital identities based on the LEI and is expected to continue along this route as greater adoption of the identifier adds to its potential.

A concern here, and across all sourcing and use of entity data, is to ensure the data is used ethically.

For now, Brexit has had no effect on the UK’s adoption of AMLD5 legislation and is unlikely to affect the implementation of AMLD6, but it will be important to keep a close eye on emerging agreements between the European Commission and UK Government that could make changes.

Meantime, there can be no doubt that further development of entity data quality and management is critical to gaining a full understanding of customers, stemming money laundering, and using the data to better effect in business development.

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Glossary

AI – Artificial intelligence

AML – Anti-Money Laundering

API – Application programming interface

BIC – Bank Identifier Code

CTF – counter terrorist financing

EMIR – European Market Infrastructure Regulation

ESMA – European Securities and Markets Authority, an EU authority that works to promote investor protection and stable and orderly financial markets

FinCen – US Financial Crimes Enforcement Network

FRTB – Fundamental Review of the Trading Book

FSB – Financial Stability Board, an international body that monitors and makes recommendations about the global financial system

GLEIF – Global LEI Foundation that acts as the operational arm of the Global LEI System

Global LEI System – a global system comprising the Regulatory Oversight Committee of the LEI, the Global LEI Foundation and Local Operating Units

ISIN – International Securities Identification Number that uniquely identifies a financial security

KYC – Know Your Customer, the process companies must go through to identify and understand clients before conducting financial business with them

LEI – Legal Entity Identifier designed to provide unique entity identification in financial transactions

LOU – Local Operating Unit within the Global LEI System that issues LEIs

MAR – Market Abuse Regulation

MiFID II – Markets in Financial Instruments Directive II

MiFIR – Markets in Financial Instruments Regulation

OFAC – Office of Foreign Assets Control of the US Department of the Treasury that administers and enforces economic and trade sanctions

OTC – Over-the-counter or off-exchange trading between two parties without the supervision of an exchange

PEP – Politically exposed person, an individual who holds a trusted and prominent position that could be abused for the purpose of money laundering or other financial offences

PermID – Thomson Reuters entity identifier

ROC – Regulatory Oversight Committee with ultimate responsibility for the Global LEI System

SDNs – Specially Designated Nationals under OFAC screening and sanctions rules

SFTR – Securities Financing Transactions Regulation

UTI – Unique Trade Identifier designed to provide unique trade identification in financial transactions

XBRL – eXtensible Business Reporting Language provides a free and global framework for exchanging business information

entity data management handbook 2020 · a-team group chief executive officer angela wilbraham...

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