Advancements in Legal Entity Data Quality

KINGLAND.COM

Advancements In

Legal Entity Data Quality

PRESENTED TO:

SIFMA Operations Conference

May 4, 2016

Discover the Confidence of Knowing.

Today’s Topic: Advancements in Legal Entity Data Quality

Tony BrownleePartnerHead of Data Science & Research

• Involved with legal entity data since 2003

• International Business Entity Identifier (IBEI) in 2007

• Legal Entity Identifier (LEI) in 2011-present

• Consolidated Audit Trail (CAT) 2013-present

• Executive “data nerd”

Get to Know Me…

Our discussion today.

My goal today:

Empower executives to ask better data questions

The industry is in a perpetual state of “data remediation”.

Legal Entity data is a top problem.

• Legal Entity Identifier (LEI) is starting to help

• Regulations from Dodd-Frank to BCBS are driving needs

• Consolidated Audit Trail (CAT) is looming and will drive more efforts

• There’s an opportunity to “own” more of this data enterprise wide

Kingland Point of View:

• Legacy data quality approaches are slow and costly. Diagnostics can help.

• Cognitive is finally here and is viable for data strategies.

• DMM and DCAM standards will become industry standards to guide quality efforts.

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Customers

Trading Counterparties

Issuers of

Financial Instruments

Suppliers

Banking & Capital Markets

Example of Legal Entity Data in Financial Services

697372940869MORGAN STANLEY.

697372964748MORGAN DOMESTIC

HOLDINGS, INC.

697372966125MORGAN STANLEY

CAPITAL MANAGEMENT, LLC

CONTROL

697372985793MORGAN STANLEY

& CO. LLC

CONTROL

CONTROL

Example: More than 2,000 legal entities comprise the Morgan Stanley hierarchy

Regulatory ReportsRegulatory

ReportsRegulatory

Reports

Client Business Strategies

Operational RiskMonitoring

Market RiskMonitoring

Enterprise RiskMonitoring

Global Business Strategies

Legal Entity Data is a Big Data problem

Names

Addresses

Classification

Hierarchy

CLP Power Hong Kong Financing LimitedCLP PWR HK FIN LTD

25 CABOT SQUARE, CANARY WHARF, Great BritainRegistered Country: British Virgin Islands

Finance CompanySpecial Purpose VehicleInvestment Trust

MORGAN STANLEY & CO. INTERNATIONAL PLCMorgan Stanley

Example Entity

2,000,000+ entities x hundreds of data points x 100,000+ potential sources = BIG DATA

Legal entity data is a high-priority, complex challenge for data programs

10,000 to 2,000,000 Number of legal entities critical to most institutions

(typically represented as clients, counterparties, issuers, suppliers, accounts, or other market

participants in many different systems)

450,000+LEI’s issued throughout the

Global Legal Entity Identifier System

200,000,000+Companies covered by legacy data

vendors, making it difficult to identify and use the most important entity data

6Major categories of legal entity data

commonly treated the same (Companies, Funds, SPVs,

Governments, Individuals, and Trusts)

8 to 500Data elements on each legal

entity record in different financial services systems

3% to 20%Average duplication rates in most legacy systems

200+ Jurisdictions worldwide, each with different rules,

standards, and quality expectations

180+Types of entity events related to mergers, acquisitions and other

corporate action types that impede data quality progress

Legal Entity Data Quality

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Discuss: Where do you spend you time? Your budget?

Assess Data Quality30%

Cleanse Data50%

En-rich

Data10%

Maintain Data10%

Assess Data Quality10%

Cleanse Data25%

Enrich Data25%

Maintain Data40%

Which one are you?

Our Data Discussion.

Assess Data Quality Cleanse / Improve Data Quality

Enrich / Expand DataMaintain Data Quality

Names Addresses

Hierarchy

Top 5 Legal Entity Data Challenges

Industry Classification

Duplication1

2 3

4 5

Questions to Ask

Legal Entity Challenge: Duplication



Top Problems

• Legal entity matching is entity-type specific (e.g. Funds, SPVs)

• Missed corporate actions introduce new entities from set-up processes

• Client and counterparty set-up processes lack consistent duplicate prevention controls

• Integration of additional sources perpetuates more duplication

• One system or multiple?• Where is the duplication

originating?

• Do you have the SMEs that understand duplication?

• Are you buying data?• What’s the total cost in data,

process, technology?

• Are we expanding the number of entities covered?

• Have we prepared for merger / acquisition onboarding?

• Does our maintenance align with findings from assess?

• Are operations and technology collaborating?

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Example: Duplication Trend Analysis

Unknown2006

20082010

20122014

0

20000

40000

60000

80000

100000

120000

Unknown2004

20062008

20102012

20140

10000

20000

30000

40000

50000

60000

70000

80000

Created Date Volume Rate2005 4,547 2.37%2006 5,587 2.91%2007 5,936 3.09%2008 5,793 3.02%2009 6,350 3.31%2010 9,492 4.95%2011 14,192 7.39%2012 13,723 7.15%2013 11,986 6.24%2014 12,092 6.30%2015 3,852 2.01%Unknown 98,382 51.26%Total 191,932 100.00%

Update Date Volume Rate2003 6 0.00%2004 3,254 1.70%2005 4,698 2.45%2006 3,231 1.68%2007 5,367 2.80%2008 6,221 3.24%2009 10,875 5.67%2010 8,019 4.18%2011 13,443 7.00%2012 13,822 7.20%2013 13,520 7.04%2014 21,443 11.17%2015 15,709 8.18%Unknown 72,324 37.68%Total 191,932 100.00%

Created Last Updated

Legal Entity Challenge: Names



Top Problems

• What source do you use? Not all sources are created equal.

• Legal name vs. Doing Business As name vs. Legacy Name

• Third Party Accounts (FAO, FBO)

• Missed Corporate Actions

• Do you have a standard?• Do you have a “that’s the way

we do it here” problem?

• Are there legacy system constraints?

• How many names do you really need (e.g. business, regulatory)?

• Can alternate names expand flexibility?

• Should legal form (e.g. Inc.) be a separate field?

• How can third parties be used with controls, reviews?

• Can you integrate with CRM and compliance processes?

Names…JP MORGAN INVEST MGMT JP MORGAN INVESTMENT MGMT JP MORGAN INVESTMENT MGMT INC JPM INVESTMENT MANAGEMENT JPMIM 1410 Great Clips of India1410 Great Clips of IndianaTOWN OF NANTUCKET, MASSACHUSETSNANTUCKET, MASSACHUSETS (TOWN OF)

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Legal Entity Challenge: Addresses



Top Problems

• ISO codes or not?

• Registered, Physical, Mailing

• Impact of branches

• Many entities at same location


• Do you have consistency across addresses?

• Have you considered jurisdictional differences?

• Have you considered cross-field consistency checks?

• Are input processes consistent with cleansing activities?

• Do you need “custom” regions (e.g. sales) aligned with ISO standards?

• Future : geo-coding?

• Can you use cognitive to drive location changes?

• How best to sync w/ LEI and/or client-driven updates?

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Legal Entity Challenge: Industry Classifications



Top Problems

• Industry classification is subjective and error prone

• Multiple standards such as SIC, NAICS, NACE, GICS, and even client-specific

• They’re missing! Legacy processes don’t require industry classification

• Inconsistencies across standards – SIC to NAICS, NAICS 2009 to 2012

• Do you have a standard?• How are classifications being

used?

• Can you align within hierarchy data for additional quality control, efficiency?

• Matching strong enough to use third party sources?

• Are there needs for granular “% of Revenue” expansion?

• One classification or multiple?

• Are your change rates for classification lower than other maintenance processes?

• Are you using M&A events as a review trigger?

Automotive?

Financial Services?

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Legal Entity Challenge: Hierarchy



Top Problems

• Risk vs. Legal vs. Sales hierarchies

• Entities are “default” linked Ultimate Parent

• Missing intermediate parent entities

• Security master data “pollution”

• <50% ownership gaps in coverage


• Do you have authoritative sources?

• Can you compare or are you using a sampling or targeted assessment?

• Do you have the required SMEs engaged for this complex data?

• Have you fully assessed downstream system impact?

• Immediate, ultimate parent vs. full hierarchy?

• Consider % ownership, non-ownership relationships?

• Have you considered strategic third party relationships?

• Are you waiting for LEI? (many years away)

BlackRock Euro Core Bond Fund

OEIC Sub-fund

Portfolio

BlackRock Institutional Pooled Funds PLC

OEIC

Investment Company

Investment Rel

BlackRock Asset Management Ireland

LimitedManager

Advisor

Advisory Rel

BlackRock Group Limited

Parent Entity

Control Rel

BlackRock International Holdings, Inc.

Parent Entity

Control Rel

BlackRock Advisors Holdings, Inc.

Parent Entity

BlackRock Financial Management, Inc.

Parent Entity

BlackRock Inc.

Ultimate Parent

BlackRock Bond Index Fund

Portfolio

Securities

Issuer Rel

BlackRock Funds III1940 ACT Investment

Company

Investment Company

Investment Rel

BlackRock Fund AdvisorsInvestment Advisor

Advisor

Advisory Rel

BlackRock Institutional Trust Company, N.A.

Control Rel

BlackRock Delaware Holding Inc.

Parent Entity

Parent Entity

BlackRock Holdco 6, LLC

Parent Entity

Control Rel

BlackRock Holdco 2, Inc.

Parent Entity

Control Rel

Control Rel

Control Rel

Control Rel

BlackRock Holdco 4, LLC

Parent Entity

Control Rel

Securities

Issuer Rel

Control Rel

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Example: Hierarchy Diagnostic Analysis

Best Practice: Tell Stories through Demographics

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• Establishes and manages the control environment necessary to achieve objectives

• Develops business case and funding management to meet stated priorities

• Focused on vision and objectives of the enterprise data management program

• Establishes expected benefits, organization and functions, and priorities of the EDM program

Ensures data requirements are effectively defined, met, and managed throughout the data lifecycle (origination, archive, obsolescence) and across all enterprise systems

Data Management Strategy Data Governance Data Quality

Data Operations

Evaluates resiliency of all the activities from process and risk management, through measurement of performance, to ensure that the EDM program is meeting organizational needs

• Established quality objectives and criteria for critical data

• Ensures management of business processes and data are designed to achieve quality objectives

Data Platform & Architecture Supporting Processes

Data Governance & Maturity provides a framework for improvement

Ensures architecture and systems that support critical data are explicitly designed, monitored and managed to meet organizational data requirements

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We deliver Data Governance & Maturity services through three different engagement models to help companies

• Assess data governance and management maturity

• Leverage industry best practices

• Deliver actionable recommendations and confidence to data programs

Engagements are led by a certified Enterprise Data Management Expert and supporting staff, tailored to your situation.

• Participants understand data governance and management deeper

• Leadership receives actionable recommendations and confidence in the current and future state of data programs

Kingland provides practical, industry-tested expertise you can trust

A s s e s s m e n t & Tra i n i n g

D ata M at u r i t y A p p ra i s a l

G a p A n a l y s i s & Roa d m a p

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Kingland’s Strategic Approach to Reference Data Management

Assess &

Cleanse

Enrich&

Maintain

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Need Some Direction?

Advancements in Legal Entity Data Quality

Technology

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