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Reference Data Management Maturity: Ripe for Change

Prepared for:

Reference Data Management Maturity: Ripe for Change February 2016


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TABLE OF CONTENTS INTRODUCTION .............................................................................................................................................. 3

METHODOLOGY ........................................................................................................................................ 3

CALL THE CHIEF ............................................................................................................................................... 4

DRIVERS FOR CHANGE .................................................................................................................................... 6

A MATURING FUNCTION ................................................................................................................................ 9

CONCLUSION ................................................................................................................................................ 12

ABOUT SWIFTREF ......................................................................................................................................... 13

CONTACT ................................................................................................................................................. 13

ABOUT AITE GROUP...................................................................................................................................... 14

AUTHOR INFORMATION ......................................................................................................................... 14

CONTACT ................................................................................................................................................. 14

LIST OF FIGURES FIGURE 1: DATA MANAGEMENT LEADERSHIP AT TOP 100 BANKS BY AUM .................................................. 4

FIGURE 2: A SELECTION OF REGULATIONS IMPACTING REFERENCE DATA MANAGEMENT .......................... 7

FIGURE 3: THE AITE GROUP DATA MANAGEMENT MATURITY CURVE .......................................................... 9

FIGURE 4: THE UTILITY MODEL ..................................................................................................................... 10



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INTRODUCTION

Regulatory and market-infrastructure-led requirements have focused the financial services

industry’s attention on managing key sets of reference data. This includes the application of new

and existing standard identifiers, such as Business Identifier Codes (BICs), International Bank

Account Numbers (IBANs), and Legal Entity Identifiers (LEIs) as well as national bank codes and

other domestically applied identifiers. The imperative is to make sure this data is high-quality

and identifiers are consistently applied to reduce payment or settlement failures, ensure

regulatory reporting is timely and accurate, and confirm that appropriate levels of risk

assessment and management are in place.

This white paper, commissioned by SWIFT’s reference data service, SWIFTRef, examines the

market’s state of data management in terms of leadership, priorities, investment drivers, and

challenges.

METHODOLOGY

This white paper is based on a closed-door roundtable, organized by SWIFTRef and conducted

during the Sibos conference in Singapore in October 2015, and on Aite Group’s ongoing data

management research.



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CALL THE CHIEF

The last few years’ emphasis on improving risk management under the specter of the incoming

Basel III framework has forced many banks to assess the quality and the management of their

core reference data sets. Reporting to regulators and clients has also escalated in this

environment, which, in turn, has increased banks’ internal data aggregation, storage, and

management requirements. This has given rise to the establishment of more data governance

programs within firms across the industry and the creation of C-level positions dedicated to

championing data management and data governance at the enterprise level. Aite Group

research indicates that as of the end of October 2015, 24% of the top 100 global banks, ranked

by value of assets under management (AUM),1 had a chief data officer (CDO) in place. Some of

these banks had even hired more than one CDO to focus on their various divisions and data sets.

Figure 1: Data Management Leadership at Top 100 Banks by AUM

Source: Aite Group

Whether they are called CDOs or heads of reference data management, these individuals face a

tough task ahead; the effective management of data across operational and geographic silos. On

the governance front, these firms must introduce a culture of responsibility and ownership of

data within the business as well as identify key data assets for improvement, and implement

programs to achieve that goal.

At a basic level, the practice of reference data management in a financial institution context is

focused on supporting various sets of data that allow a firm to identify, describe, classify, verify,

and link items such as instruments, legal entities, and corporate actions as well as support and

identify the processes related to interactions with counterparties and clients.

Reference data is commonly referred to as “static” (though it is typically far from static); it tends

not to change frequently when compared to other types of data, such as market data. For

1. SNL Financial’s ranking of the world’s largest banks, August 2015.

8% have more than one CDO

16% have one CDO

76% do not have a CDO



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example, most of the underlying data related to a financial instrument will remain the same for

the lifetime of that security and beyond, should that data need to be reported on a historical

basis; market data, on the other hand, can change on an intraday basis. Data related to legal

entities or accounts tends to change much more than that related to instruments due to

activities such as mergers and acquisitions.

The goal of any team focused on reference data management is to ensure that this data is

adequately sourced, classified, validated, reconciled, cleansed, integrated, enriched, stored, and

distributed to downstream consuming systems. There is, however, no one-size-fits-all approach

to tackling data management, and the overarching strategy is often determined by a firm’s or

division’s operational priorities, internal infrastructure, and strategic and tactical projects related

to functions such as risk management or compliance. These dynamics also have a significant

impact on investment level and the route that a firm chooses to take from a data management

technology standpoint.

A vendor roundtable panelist notes that reference data is dynamic, and the inclusion of

contextual information or metadata to capture the relationship between data sets, such as

counterparty information and transactional data, is important to ensure the data is fit-for-

purpose. “The value of reference data is much greater within a repository, surrounded by

metadata, than without,” he says.



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DRIVERS FOR CHANGE

The regulatory environment has been and still is a significant challenge and driver for change in

the data management universe due to the increased volume and granularity of reporting

requirements. Not only do these regulations increase the volume of reported data, they also

directly scrutinize the quality of the data and require firms to adopt new standards and

classifications, and provide a data audit trail. To this end, the Basel Committee on Banking

Supervision (BCBS) 239 compels banks to adopt new data management principles focused on

supporting risk-management reporting and governance. Tier-1 banks must adopt the data

aggregation principles by January 2016, which will necessarily involve a focus on improving data

ownership by the business and the introduction of consistent data quality frameworks across

geographies and the enterprise.

The Anti-Money Laundering Directive IV (AMLD IV) is a significant challenge for legal-entity-

focused compliance teams, as it requires a more risk-based approach to anti-money laundering.

Firms must determine the level of risk posed by a client before applying the appropriate level of

due diligence and to identify ultimate beneficial ownership details, all of which entails a single

centralized view of a client and its legal entity relationships.

The implementation of the Single European Payments Area (SEPA) mandated banks and

corporations to use identifiers, such as IBANs and BICs, to initiate and validate SEPA payment

instructions within a SEPA country. In February 2014 they became the only acceptable

beneficiary customer account identifier and bank routing designation for euro payments in SEPA

countries, and in October 2016, this regime will be extended to euro payments in non-euro-area

countries.

The global taxation change agenda has also increased banks’ focus on legal entity data and the

complexity of monitoring and maintaining that data. The Foreign Account Tax Compliance Act

(FATCA) requires firms to identify clients who are considered by the U.S. Internal Revenue

Service (IRS) to be eligible to pay tax in the United States. Internationally, the Organization for

Economic Cooperation and Development (OECD) Common Reporting Standards (CRS) are due to

introduce a new multi-jurisdictional tax information reporting regime, which will be based on tax

residency rather than citizenship (as under FATCA). Both of these taxation rule changes

significantly impact the management of legal entity data.

An Eastern European bank roundtable panelist indicates that the firm is obliged to present and

maintain the ownership structure of international clients and store documentation linked to the

relevant identifiers on an ongoing basis, which is a significant challenge. The most onerous

component of the regime is the retention and tracking of hard-copy documentation, which the

national central bank mandates must be kept for a minimum period of five years. The gathering

of this data is a much slower process than it is for electronically based information, and overseas

client-data gathering is especially challenging.

Figure 2 overviews some of the incoming regulations that will impact data management,

including Basel III, which is being implemented at the national or regional level across the globe.



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Figure 2: A Selection of Regulations Impacting Reference Data Management

Source: Aite Group

It is not just regulation that is driving a focus on data management; cost cutting is also a factor.

Traditionally, it has been difficult to calculate the cost of the lack of a robust and comprehensive

centralized infrastructure for reference data management because the operating cost of

supporting the middle and back office has often been hidden under the bracket of “keeping the

lights on.” The current economic climate has, however, thrown a spotlight on the total cost of

operations, from trading to settlement; hence, there has been some level of assessment of

internal reconciliation processes required to support key data sets within a number of financial

institutions.

To keep the costs of manual processes down, offshore centers in places like the Philippines and

India remain popular for the location of teams dedicated to data cleansing. The gradually rising

cost of labor in offshore centers and a greater awareness of these centers’ total cost in terms of

logistical issues and high levels of staff turnover have brought them increased C-suite scrutiny.

Data privacy as an issue has also increased in importance over recent years, and panelists agree

that there is a need to have clear governance for what individuals can and cannot access.

The biggest ongoing reference data challenges for firms in the current market follow:

Direct regulatory scrutiny of the quality of the data underlying reporting and a

growing list of regulator-specified data formats

2016 2017 2018

June 2016Due diligence for

pre-existing accounts

June 2017AMLD IV to enter

into force

AMLD IV

2016 to 2018Ongoing implementation

of Basel III framework

31 May 2016Annual tax report for previous year

September 2016Annual reporting by local authorities to

IRS for 2015 to include aggregatedforeign reportable

amounts paid

January 2016CRS comes into effect for early-adopter OECD

countries

FATCA and CRS

Basel III

January 2016Deadline for

implementation of BCBS 239

BCBS 239

January 2016BIC no longer mandatory for

cross-border SEPA credit transfers.

Niche products deadline for migration to SEPA Credit

Transfer/SEPA Direct Debits

SEPA

October 2016SEPA deadline for euro

denominated payments in non-euro area countries



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The requirement to store and report large quantities of data

The heightened competitive environment, in which business is won on the ability to

better service clients and expand more quickly to cover new markets and services

The need to cope with market structure change, such as a shortening of the

settlement cycle and the continued introduction of SEPA arrangements

The continued desire to reduce inefficiency and cost by eliminating manual

processes

Higher levels of mergers and acquisitions and the imperative to realize cost

reduction and efficiencies in the internal post-merger environment

The focus on better supporting risk management by providing greater granularity on

the data on which risk calculations have been based, as required under Basel III, and

the ability to correctly classify regulatory capital



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A MATURING FUNCTION

The challenges of maintaining a golden copy of data are well-articulated, but another critical

factor in achieving data management maturity is ensuring that the business is engaged and

connected. A European bank roundtable panelist says sustaining the balancing act between

dealing with a centralized database and maintaining efficient market operations is a far from

simple task. Numerous applications across the bank are reliant on reference data, but they need

to be efficiently connected to a centralized database to benefit from any data cleansing and

management work that goes on. For example, Know Your Customer and compliance data is

reliant on accurate BICs and IBANs, and these sort codes need to be efficiently stored and

maintained as well as distributed to downstream consuming systems in a timely manner.

A European clearing bank panelist highlights the need to foster ownership and responsibility

from reference data’s business consumers via stewardship programs. Internal business users are

reluctant to take responsibility for data quality, which means it can be difficult to ensure data is

correct at the end point, he says. Achieving a connected and engaged end-user community via a

strong data management framework and a governance program is therefore a sign of increased

data management maturity (Figure 3).

Figure 3: The Aite Group Data Management Maturity Curve

Source: Aite Group

Very mature (most mature): A clearly defined data governance framework is in place, data aggregation is possible in an intraday context for all key data sets, and data quality is reliable in the long term

Mature: Data quality is high, a wider data governance framework is being introduced, and the firm is much more strategic about regulatory compliance projects that impact data sets from across the business

Fairly mature: Data is fairly clean and quality is relatively high, but data management is still tactical rather than strategic

Fairly immature: Some work is being done to reconcile data across end-user systems and cleanse data, but aggregation is still challengingImmature (least

mature): Data resides in end systems, is hard to extract, and is poor quality overall



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Legal entity data is slightly behind the maturity curve when it comes to management compared

to financial institutions’ other reference data sets. Until relatively recently, very few firms

employed centralized teams in charge of managing legal entity data. In the current environment

of heightened risk-management awareness, keeping track of legal entity data has, however,

become a high priority. Firms seeking to assess exposure to the ultimate parent company of any

firm must accurately capture the changing parent/child dynamics within this data, especially

when that parent company is classified as a “systemically important financial institution.”

Gathering legal entity and account data from multiple sources across the industry is not a simple

task, especially for smaller and midsize firms with fewer internal resources to dedicate to the

task. This explains the increased popularity of the utility model for reference data, which has

become a significant industry talking point over the last 12 months. To this end, the European

clearing bank panelist notes that an entity that can act as a central source for reference data

coming from multiple providers is appealing. The concept of a centralized industry database for

all account-related data assets, such as BICs, IBANs, and LEIs, means that there is one source for

the industry to contribute to and one source from which it can consume this data (Figure 4).

Figure 4: The Utility Model

Source: Aite Group

The benefits of a centralized utility for reference data include the following:

Reducing manual effort for firms in gathering and cleansing reference data: Firms

can redeploy resources in other, more competitively valuable areas.

Reference data utilityData cleansing and checks, refresh,

and maintenance

Data contributors (corporations and banks)

Data providers

Data consumers (corporations and banks)



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Providing firms more predictable ongoing costs of operation: The cost of managing

data could be easily identified and controlled via a service rather than the variability

of internal support.

Taking away some of the cost and complexity of responding to changing regulatory

requirements for firms: Rather than engaging in separate compliance projects, firms

can instead rely on the utility to interpret data-related requirements of incoming

regulation.



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CONCLUSION

Data management has become more of a priority for financial services firms.

Spending on reference data management is gradually rising year on year in response

to business, regulatory, and risk-management pressures, not least of which is the

steady rise in the volume of data that must be managed and maintained for

compliance purposes.

Data governance and stewardship is increasingly important. Ensuring that

downstream-specific requirements for data formats are met is important because it

means the data is fit-for-purpose. Engaging business users via stewardship programs

is a step toward achieving this level of maturity.

The utility model is becoming more popular in the data realm. The concept of a

centralized industry database for all account-related data assets, such as BICs, IBANs,

and LEIs, means that there is one source for the industry to contribute to and access

to consume this data; hence, the cost of sourcing this data is more predictable, and

the complexity of the process is reduced.



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ABOUT SWIFTREF

SWIFTRef, the global payments reference data utility, is SWIFT’s unique reference data service.

SWIFTRef offers a single source for all the reference data needed for a flawless payments process

and accurate regulatory reporting for financial institutions, corporations, and data or service

providers. If identification and validation of BICs, IBANs, national bank codes, standing

settlement instructions, SEPA routing information, or other reference data are challenging on a

daily basis, then discover how SWIFTRef has it all figured out.

SWIFTRef sources data direct from data originators, including central banks, code issuers, and

banks. SWIFTRef provides standard data collection tools and makes it easy for issuers and

originators to maintain data regularly and thoroughly. SWIFTRef constantly validates and cross-

checks data across the different data sets.

Expert support is available through the help desk, our data collection support for data owners,

and our partner program for solution providers. The SWIFTRef labelling scheme gives users

assurance when choosing services from solution providers.

SWIFT is the member-owned cooperative through which the financial world conducts its

business, and, because of its industry role and relationships, SWIFT is uniquely placed to deliver

accurate and comprehensive payments reference data: SWIFT is the ISO registry for BICs and

IBAN formats.

CONTACT

For more information, please contact:

For customers in EMEA [email protected]

For customers in APAC [email protected]

For customers in the Americas [email protected]



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ABOUT AITE GROUP

Aite Group is an independent research and advisory firm focused on business, technology, and

regulatory issues and their impact on the financial services industry. With expertise in banking,

payments, securities & investments, and insurance, Aite Group’s analysts deliver comprehensive,

actionable advice to key market participants in financial services. Headquartered in Boston with

a presence in Chicago, New York, San Francisco, London, and Milan, Aite Group works with its

clients as a partner, advisor, and catalyst, challenging their basic assumptions and ensuring they

remain at the forefront of industry trends.

AUTHOR INFORMATION

Virginie O’Shea

[email protected]

Contributing author:

Will Woodward [email protected]

CONTACT

For more information on research and consulting services, please contact:

Aite Group Sales +1.617.338.6050

[email protected]

For all press and conference inquiries, please contact:

Aite Group PR +44.(0)207.092.8137

[email protected]

For all other inquiries, please contact:

[email protected]

mailto:[email protected]





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