patni wp data management implications of forthcoming systemic risk regulations

WHITE PAPER

DATA MANAGEMENT IMPLICATIONS OF FORTHCOMING SYSTEMIC RISK REGULATIONS

The Dodd-Frank Wall Street Reform and Consumer Protection Act (DFA) was signed into law on July 21, 2010. The bill represents the most significant change to financial regulation in the United States since the world wide economic downturn of the 1930’s, and transfers all financial regulatory authority to the Comptroller, the Federal Deposit Insurance Corporation and the Federal Reserve Bank. It will enable transparency of the financial markets, provide investor protection, and protect consumers from predatory lending practices.

This paper will focus on implications of the DFA act, which defines the Office of Financial Research (OFR) and will significantly impact every buy-side and sell-side firm that underwrites, initiates, executes, clears, or settles trades. This paper is written during a period of transition where short-term events can modify the interpretation of the DFA in its definition of the OFR’s practical implementation.

This document is an opinion piece on how the OFR might approach its responsibilities and given the many uncertainties at the time of writing, what firms can sensibly do now with a good chance that those preparatory activities will be useful in accelerating a response to reporting mandates and are unlikely to be wasted.

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WHAT THE REFORM BILL ESTABLISHES

Established under the signing of the Dodd-Frank Act are the Financial Stability Oversight

Council (FSOC) and the Office of Financial Research (OFR). The FSOC and the OFR

are among the dozen regulatory agencies that will be responsible for writing nearly 240

new rules and over 65 studies for the purpose of gathering and analyzing data for

monitoring systemic risk.

FINANCIAL STABILITY OVERSIGHT COUNCIL (FSOC) The Dodd-Frank Act takes the existing regulatory framework where agencies oversee

specific segments of the industry and consolidate all regulatory oversight within one

agency: the Financial Stability Oversight Council. The FSOC has a clear mandate to

promote market discipline, identify system risk, and respond to emerging risks and threats

to the stability of the financial markets of the United States. It is accountable to Congress

and the American people and will report to Congress annually and as necessary or

requested by Congress.

With its new authority, the FSOC is authorized to:• Coordinate activities among member agencies regarding policy, rulemaking,

examinations, reporting and enforcement

• Facilitate the collection and sharing of information among member agencies

• Identify and designate nonbank financial companies for supervision

• Identify and designate market utilities as systemically important; requiring them to meet the risk management standards established by the regulatory authorities

• Identify actions to break up firms deemed “grave threats” to the financial stability of

the US markets

• Recommend new, stricter reporting standards for the large, complex, interconnected banks, and nonbanks

The FSOC can also provide direction to the OFR and can request data and analysis from them.

OFFICE OF FINANCIAL RESEARCH (OFR)The OFR was established to improve the data gathering and analyses for the FSOC

and the regulatory authorities. It will be housed in the Treasury Department and provide

financial data and analysis to the FSOC and its member agencies in support of an

agency’s effort to regulate financial institutions.


Important new agencies

established under the

Dodd-Frank Act are Financial

Stability Oversight Council

(FSOC) and the Office of

Financial Research (OFR).

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According to the DFA, the OFR will establish the standards for financial reporting and

improve the quality of data it receives from buy-side and sell-side firms and from

systemically important bank and nonbank entities. The OFR will include a Research

and Analysis Center to provide analytics and tools necessary to monitor systemic risk

in the markets.

The OFR will have a Director appointed by the President and confirmed by the Senate.

The Director will hire staff and engage outside firms and academia to develop the

analytics and tools required to analyze, identify, and report on systemic risk.

The OFR will have primary responsibility to:

• Develop standards for the types and format of the data

• Collect data from financial institutions and systemically important bank and non-bank entities

• Monitor, investigate, and report on changes in system-wide risk levels and patterns

• Maintain expertise to support analytical requirements of financial regulators

• Investigate disruptions and failures in the financial markets and make

recommendations

• Conduct studies and providing advice on the impact of policies related to

systemic risk

• Promote best practices for financial risk management

• Develop tools for risk measurement and monitoring

• Report to the FSOC and Congress on market developments and potential emerging threats to financial stability

In addition to these responsibilities, if the OFR’s analysis deems it necessary, it can

recommend to the FSOC and Congress “heightened prudential standards” regarding:

• Risk-based capital

• Leverage

• Liquidity

• Contingent capital

• Concentration limits

• Enhanced public disclosure

• Overall risk management

While the industry waits for the OFR to provide rules and guidance on its reporting requirements, what initiatives can buy-side and sell-side firms be undertaking now to prepare?

The remainder of this paper offers some insights, suggestions, and practical advice. With

the aggressive timescales likely to be required by the OFR, if firms wait for the details to

unfold before taking preemptive actions, they may be unable to respond in time.


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HOW MIGHT THE OFR APPROACH ITS RESPONSIBILITIES?

The OFR must report to Congress in July 2011 on its progress implementing its organization

and infrastructure. In the following July it will report to Congress on the state of systemic risk.

This creates a very aggressive timescale for the OFR to define the multiple necessary

analyses, gather data, aggregate, and report on it. There is likely to be extreme pressure to

create new security and counterparty identification schemes outside the normal lengthy

industry consultation processes. Indeed, a request for the industry to suggest the format and

process for creating a standard Legal Entity Identifier (LEI) was announced on November 23,

2010. This aggressive stance will also likely be repeated for firms which will have to perform

analyses required in short timeframes and probably also submit trade information.

In the period before the OFR was written into law, two very useful documents were

released that give great insight into the systemic risk analyses likely to be needed and the

data requirements that will flow from them. They are the National Academy of Sciences

(NAS) Report from a workshop held in November 2009, and the SIFMA Report performed

by Deloitte LLP in June 2010. These two reports are summarized below as they are a

necessary precursor to understanding why we are making the suggestions we do, for a

preparatory data management response.

NAS REPORT SUMMARYIn August 2009 Senator Jack Reed of the Senate Banking Committee wrote to the National

Academy of Sciences (NAS). His position was that the regulators faced limitations in data and

automated tools available to identify and mitigate potential systemic risks that cut across

financial institutions, products, and regulators. He requested the NAS to prepare an analysis

of existing data and analysis tools within the regulators, the data collection and analysis needs

to address systemic risk, the resources, and technical challenges and options available.

It is worthwhile to understand the main points of this report as they form a significant

backdrop to the formation of the OFR and how it might approach its task.

Some of the major themes of the report are:

1. The need for a common language for securities and entities

2. The data needed for systemic risk monitoring

3. The signals that a regulator might monitor

4. Monitoring networks of counterparty risk exposure

5. The need for new analytical tools


The National Academy of

Sciences (NAS) Report and the

SIFMA Report provide a great

insight into the systemic risk

analyses likely to be needed and

the data requirements that will

flow from them. They are a

necessary precursor to

understand how OFR might

approach its responsibilities.

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THE NEED FOR A COMMON LANGUAGE FOR SECURITIES AND ENTITIESThe systemic risk regulator will have to unambiguously aggregate and interpret each

firm’s risk data. Today there are a number of structural issues for data that significantly

inhibit this goal. Primary among these is that there is no industry standard non-proprietary

counterparty or legal entity identifier. Each firm either creates their own set of unique

identifiers or uses those of their counterparty data provider. A regulator seeking to

aggregate counterparty exposures cannot do so without an industry standard entity

identifier. Secondly, there is no standard security classification code or transaction type so

a regulator would not be able to run analyses across asset classes in a uniform way.

A regulator will need to understand detailed terms of a complex OTC derivative to

aggregate risk and counterparty commitments. As such there are no standard ways of

defining the detailed attributes of complex instruments so each firm takes its own

approaches and any aggregation to a regulator would have to resolve inconsistencies.

THE DATA NEEDED FOR SYSTEMIC RISK MONITORING The regulator will need to make judgments on when certain firms or market segments are

over leveraged, when asset bubbles are growing, when exposures are becoming correlated,

and many others. Collecting raw transaction data is not enough. Context will be needed, for

instance, a firm’s sustainable leverage is dependent on the underlying health of the firm,

and the amount and types of stresses in the system. The linkages that contribute to

systemic risk and how crises propagate in interconnected markets are not well understood.

Knowing positions alone, for example, does not indicate if a liquidity freeze will occur.

There were conflicting views on the frequency and granularity of data collection ranging

between 'more is good' and 'more is harder to analyze', the summary being that the

analytic models will drive the data requirements and that those models are not yet built

and will iterate over time. It would be impractical for regulators to return to banks every

few months with new data requirements as they create new analytic models. It is more

likely that they will initially err on the side of caution and ask for more rather than less

data. As their models increase in sophistication, the data will be in place.

SIGNALS THAT A SYSTEMIC RISK REGULATOR MIGHT MONITORSystemic risk can come from multiple points of origin. Discussions have focused on firms’ positions and transactions but there are many potential contributors, such as housing prices and other macroeconomic data. The NAS report mentioned risk concentrations, profits, and asset price escalation as likely to be monitored for potential instabilities. Counterparty relationships are necessary to provide insight into risk concentration.

Stress tests are likely to vary over the economic cycle and could incorporate aspects such as transaction velocity as well as valuation variance between mark to market and valuation models. As situations become stressed, gross exposures become more important than net exposures as it becomes harder to unwind large short or long positions as liquidity dries up.

The area is extremely difficult to regulate and is likely to see many iterative changes as

understanding improves. Right now we do not know if we can foretell instability, or even if

we could, whether corrections could be made in a controlled way, for example to prevent

herding out of an illiquid crowded trade.


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MONITORING NETWORKS OF COUNTERPARTY RISK EXPOSURESFirm specific data in isolation does not illuminate all relevant sources of risk. It is also

necessary to understand the exposures to firms’ counterparties. These counterparty risks

exist in a cascade of relationships and are globalized. In order to understand the

necessary systems behavior we need to know how an initial credit event may impact

exposures in other firms. We will need more understanding of interconnectedness and

how they behave under stress conditions.

NEED FOR NEW ANALYTICAL TOOLSThe NAS reports agreed that no one model would suffice but rather a suite of coarse and

fine-grained models are required at both a macro and a micro level. These models would

have to adapt to changing networks and topologies. Currently there is no significant

analytic capability in any of the regulators in the US, so the capability will need to be built.

The question arises as to whether analysis can and should be done within firms or be run

by a regulator. The 2009 Supervisory Capital analyses (SCAP), was performed by the 20

largest firms at the height of the crisis. This approach was generally thought to be

successful with relatively simplistic like-for-like analyses. It is, however, valuable for the

regulator to have the data and analytic capability within its own domain, as it can perform

analyses more quickly without signaling its areas of concern. This reinforces the need to

develop analyses with data requirements in advance i.e., creating a super-set of data held

for analysis by the regulator.

SIFMA REPORT SUMMARYThe study was commissioned by SIFMA on behalf of its members and performed by

Deloitte LLP and seeks to promote greater awareness and understanding of potential

systemic risk information requirements. The authors used the NAS report as its starting

point and incorporated interviews of regulators, many buy and sell-side firms, exchanges,

and industry utilities.

In the course of the interviews it became clear that there was no single information

approach that would serve all the needs of a regulator or firm. Eight key systemic risk

information approaches were identified. Each has its own advantages and disadvantages

in terms of the net benefit of the approach, its resource requirements in the firm and the

regulator, and its data gaps. The approaches are summarized below.

1. Enterprise-wide stress tests — Here the regulator develop specific macroeconomic

stress tests and pass them out to the firms to run the analysis. The regulator then

aggregates, challenges, and compares the results. This is similar to the Supervisory

Capital (SCAP) tests referred to above.

2. Reverse stress tests — Here firms identify loss scenarios that will have a significant

impact upon their operations and describe mitigations. The advantage of this

approach is that different firms can have interesting scenarios that others may not

have thought of. The regulator can then aggregate these and pass back a useful

superset of scenarios to conduct as per the enterprise-wide stress tests, above.


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3. Aggregated risk reporting templates — Similar to a global Chief Risk Officer, the

regulator would develop a consistent risk reporting template across the market, which

would enable it to acquire consistent risk data across the industry which it could

aggregate.

It would be unlikely that the template would be static as it would have to evolve with

market conditions and as our understanding of systemic risk analysis evolves. It

would be most useful in a crisis rather than in steady-state conditions.

4. Risk sensitivity — Firms would provide risk sensitivity information to regulators

representing the key risk exposures of their business, which regulators can aggregate.

5. Trade repositories — Repositories are becoming more prevalent as a way of

increasing transparency within the industry. For example the CDS market has

moved from OTC to exchange traded with multiple clearers all reporting trades to

the DTCC’s Trade Information Warehouse. These repositories therefore act as a

useful source of transaction and position information that could potentially be

accessed by a regulator for the purposes of monitoring systemic risk.

6. Repositories and key industry utilities — Similar to #5 above, some firms

operate effectively as industry utilities able to provide market-wide position level

information in certain asset classes.

7. Concentration exposure reporting — Regulators develop thresholds for key firms

across products, counterparties, and markets. Firms then generate reports on name-

specific risks including individual positions and exposures to obligors and issuers

above a threshold. The regulator can then perform analytics and aggregate to an

industry-wide view on concentration exposures.

8. Data warehouse — The regulator would run a central data warehouse, which would

receive transaction and position information from all relevant firms. The data would

be granular and would be the basis of many types of analysis.

CONCLUSIONS FROM THE NAS AND SIFMA ANALYSESThe following conclusions from the SIFMA and NAS reports are speculative for the

purposes of making assumptions about the data management implications of systemic risk.

• We are highly likely to see the regulator use a hybrid of the above set of approaches.


The NAS and SIFMA reports

reiterated the need for data

comparability between firms

driving the following:

• Unique entity identification

standards

• Security classification standards

• Uniform security definition

semantics.

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• Since systemic risk analysis is a new discipline we should expect research to be funded

on new analytic techniques. These will evolve over time, and we should expect to see

the regulators mature their information and analysis requests to firms.

• Analyses will need to change over the economic cycle. Analyses needed to examine the

buildup of systemic risk are different from those needed in times of crisis.

• Given the aggressive goal of initially reporting the state of US systemic risk to Congress

in July 2012, this will likely involve techniques such as scenario analysis and template

reporting before a granular data warehouse can be built.

A useful and practical approach would combine a few of the above elements:

− Reverse stress test (2)

− Defining enterprise-wide stress tests (1)

− Complemented by aggregated risk reporting templates (3)

− Concentration exposure reporting (7).

• The data warehouse approach is theoretically optimal although huge challenges are

faced due to: the sheer volume of data, the availability of appropriate analytical models,

and staff and budget resources needed to actually build the capability. Advantages are:

− Availability of fresh systemic risk insights will result in new analytic models. They can

be implemented by distributing to firms or by running on the warehouse. It would be

far less politically challenging to perform analysis on the warehouse rather than

within financial firms via consultation with the industry.

− With a central data warehouse the regulator would be able to run more sophisticated

netting of risks, which would results in a more holistic view. With complex hedging

and trade offsetting, aggregating firms’ risk is not a simple task. The data necessary

to perform this offsetting would be lost in any individual firm’s summarized risk report.

− When close to a potential bubble, if the regulator sought to ask for further specific

information in a new report, then it would signal to the industry a potential problem

and possibly risk an adverse and uncontrolled unwinding. With a data warehouse the

regulator would be able to perform such analyses internally without sending signals

to market participants.

− The SCAP reports, while apparently successful, did put a resource strain upon

already overloaded critical resources in a time of crisis. A central data warehouse

could alleviate some of those pressures.

• Questions remain as to exactly what data would go into such a warehouse and where

that data will come from. It is plausible that the data required will be trade and position

data, across asset-class. There are many concerns, not least of which is sensitivity by

hedge funds to revealing time-sensitive trading strategies. This could be eased by

requiring reporting on a time delayed basis similar to the European MiFID regulations

that require reporting of all transactions on a T+1 basis.

Information could be sourced from firms, industry utilities, and trade repositories, or a

combination of both. The combination could be used either as a means of cross

checking or because data gaps exist as not all asset classes are covered by a suitable

utility. The regulator could take the view that the simplest way to resolve it is to mandate

that firms report all trades and positions. Alternatively regulators could seek to require

firms to contribute information only in asset classes where there are data gaps. The

takeaway is that firms will at least have to contribute some trade and position

information in at least some OTC asset classes, although this may be constrained to

sell-side firms only.


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• While not in the eight approaches summarized above, the NAS and SIFMA reports

reiterated the need for data comparability between firms driving the following:

− Unique entity identification standards

− Security classification standards

− Uniform security definition semantics.

We believe these will be introduced in an aggressive timeframe, with numerous

short-term impacts and long-term benefits to the industry. We discuss this in more

detail later in the paper.


There is also a growing consensus on

both sides of the Atlantic in the value

of a reference data utility and we can

expect aggressive build outs and great

cooperation on data sharing and

standards. Europe has established the

European Systemic Risk Board

(ESRB) with a role similar to the OFR

in the US.

LIKELY CROSS-INDUSTRY REFERENCE DATA INITIATIVES

We have alluded to the likely new standards for entity identification, security classification,

and security attribute semantics. There is also a growing consensus on both sides of the

Atlantic in the value of a reference data utility. The argument is that reference data falls

into two main categories: either factual or interpretive. Factual data would be legal entity

or securities definition information. Interpretive data would be, for example, securities

end-of-day pricing and valuation data. It is appropriate to have more than one opinion on

interpretive data, but it is counterproductive to have more than one version of factual data.

With factual data you want one authoritative source and that could be provided by the

utility for product and counterparty factual data.

If a utility did provide an authoritative source of legal entity and securities data then it

could be distributed to the existing vendor community who could then repackage and

distributed to their client base. This would retain existing business models and offer

vendors both the possibility of higher quality data and a potential lower cost base, while

giving them the opportunity, for example, to map old to new legal entity identifiers.

PRODUCT DATAFor security product data, when a new security is issued, lawyers and accountants precisely describe it in a term sheet. This sheet is used by multiple data vendors who interpret it and populate a new record for that security. For simple securities the vendors do this very well but for complex products it is easy for attributes to be missed or misinterpreted due to time pressure and human error. Investment firms spend considerable effort to cross compare product feeds for a new security and frequently intervene manually to resolve differences in opinions on what should be factual data.

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Under the auspices of the Enterprise Data Management Council, the industry has been

working on a precise semantic definition of all the attributes necessary to define a security

across asset classes. The pilot was done in the asset class of Mortgage Backed

Securities (MBS). This effort has been extended across asset classes and is nearly ready

for market introduction.

There is a growing consensus to use this semantic definition at the point of issue within a

reference data utility so that the information defining a security, when those lawyers and

accountants initially describe it, is tagged authoritatively at the point of issue. This

information would then be made available to data vendors, who would easily repackage

and distribute it without the intervening step of term sheet interpretation. This would give

the industry an accurate, precisely interpretable and consistent view of product data made

available through existing channels.

Alternatively, complex attributes could have a different interpretation between the data

vendor’s prior definition and the definition employed by the reference data utility. This

could require mapping of attributes.

COUNTERPARTY DATAMost data vendors employ large staffs cleansing counterparty and entity data. Many

investment firms also employ large staffs doing exactly the same thing, representing a

large overhead for the industry, which inevitably creates multiple opinions on the same

core facts. The problem is exacerbated by the lack of a common standard for a unique

entity identifier. Fixing the identifier issue (described below) could come quickly and

should be incorporated in counterparty data vendor’s feeds. Moving to a common source

of entity data could take longer but is a valid industry goal.

INTERNATIONAL COOPERATIONThe severity and global nature of the financial crisis has produced an unprecedented

degree of international cooperation. There has been great acceptance of the international

nature of systemic risk, and no developed country has been an immune to its affect.

Accordingly, the G20 economic group expanded the mandate of the Financial Stability

Board (FSB) in London in April 2009.

“The Financial Stability Board (FSB) is established to coordinate at the international level

the work of national financial authorities and international standard setting bodies in order

to develop and promote the implementation of effective regulatory, supervisory and other

financial sector policies. In collaboration with the international financial institutions, the

FSB will address vulnerabilities affecting financial systems in the interest of global

financial stability.”

Members of the FSB span most countries and include their central bank and banking

supervisory regulator plus the main international standards setting bodies and financial

institutions such as the BIS, the World Bank, and the IMF. Europe has established the

European Systemic Risk Board (ESRB) with a role similar to the OFR in the US. While

these organizations are just being formed, we can expect aggressive build outs and great

cooperation on data sharing and standards.

Both sides of the Atlantic are discussing the potential role of a reference data utility, as it

would make most sense to have this created on an international basis if the cooperation

can be effectively achieved.


11DATA MANAGEMENT IMPLICATIONS OF FORTHCOMING SYSTEMIC RISK REGULATIONS

LIKELY IMPLICATIONS FOR REFERENCE DATA MANAGEMENT

While there are many potential impacts on financial firms, we focus on those that we

think to be very likely, directly affecting reference data or its management within a firm.

Our suggested actions have an independent ROI so even if they do not correlate directly

with eventual OFR mandates, they will be nonetheless valuable initiatives for the firm.

Some of the relevant impacts are:

• New standards for at least entity identification

• Contract source tagging to standardize and improve the quality of product data

• Multi asset class transaction and position reporting

• Template analyses across the enterprise including Systemically Important Financial Institutions (SIFI) counterparty exposure and risk concentrations

• Pricing transparency

Each of these line items is examined in further detail below with recommendations on

how to prepare for them.

NEW ENTITY IDENTIFICATION STANDARDS The SEC is drafting a derivatives transparency rule for mid 2011, which is driving the

need for a short-term standard entity identification scheme. Recently (November 2010)

the Treasury Department issued a “statement of policy with request for comment”

specifically related to the desired characteristics for a Legal Entity Identifier (LEI) with

the objective of creating a “universal standard for identifying parties to financial

contracts”.

However, until the standards are set and implemented, the entity identifiers used within

firms will not be ‘standard’. Both buy-side and sell-side firms will most likely require

modifications to applications or will adopt means to translate from external to internal

identifiers allowing applications to run unmodified. Firms such as Avox already provide

this mapping service between internal and external identifiers.

The Know-Your-Customer (KYC) function can maintain this master counterparty data as new

counterparties are on-boarded and compared with the counterparty data provider’s records

as soon as possible in the trading process to ensure consistency.

Action items directly affecting

reference data or its management

within a firm can have an

independent ROI so even if they

do not correlate directly with

eventual OFR mandates, they will

be nonetheless valuable initiatives

for the firm.

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Higher quality entity data should reduce the need for large investments in resources for

entity data scrubbing for sell-side firms. Buy-side firms should also consider taking

advantage of this data provision by looking at new functionality such as pre-trade analysis

of counterparty concentrations as well as more obvious counterparty exposure reporting.

CONTRACT SOURCE TAGGINGIt is quite probable that product data will be standardized at the point of issue. This will not

be immediate, but when it happens it will improve the consistency of product data and

again should be distributed by existing reference data providers. As with entity id, the

semantics of individual attributes are likely to be modified and standardized.

Organizations should work with existing reference data providers when there is more

clarity on how they will carry the old and new attribute definitions. It is also quite possible

that commercial utilities will provide translation services.

Master Data Management (MDM) technology is discussed in this paper as a valuable

approach for providing easier compliance and many other direct business benefits. MDM

can also provide translation of field attributes to consuming applications, which can then

run unmodified.

MULTI ASSET CLASS REPORTINGIt is likely that the OFR at some point will require transaction and position reporting across

asset classes including OTC derivatives. The shift to derivative CCP’s backed by asset

class trade repositories means that these repositories will have the bulk of transactions

and positions in many asset classes, so in theory they should be able to perform the OFR

reporting responsibility thereby offloading firms. There is a strong expectation that

commercial utilities will also provide reporting applications that will take firms’ input in their

own internal formats and convert it to the format required by the OFR.

TEMPLATE ANALYSESBased on the findings from the SCAP reports concerning the requirements of SIFIs at the

height of the crisis, it is likely that standard analyses will need to be provided to the OFR

on a regular basis. SIFIs will likely also be subject to additional reporting. The types of

reports will probably include counterparty exposure, risk concentrations, average liquidity

etc., and the analyses will be enterprise wide. If firms maintain business silos without

centralized data governance and technology designed to integrate data between silos

they will be at a significant disadvantage.

MDM technologies can be used as a way of bringing silos of data together into a

centralized virtual data model. MDM can apply business rules to merge duplicated data

across data silos and resolve any data interpretation inconsistencies. This process moves

enterprise reference data towards a single logical data model, which assists both the

regulatory analysis and reporting needs.

PRICING TRANSPARENCYAny reference data utility is unlikely to be a provider of asset prices as these are

fundamentally interpretive, so a multiplicity of opinion is desirable. This will leave the

industry infrastructure largely unchanged as regards pricing. However, investors are

already stepping up the pressure to provide an independent, sophisticated and

transparent approach to pricing. There is a large industry body of knowledge in this area,

so we do not discuss it further.


13DATA MANAGEMENT IMPLICATIONS OF FORTHCOMING SYSTEMIC RISK REGULATIONS

TAKE ACTION NOW

Incorporating the actions from the above list, firms should consider the following:

1. Enact a centralized data management function to bring together data vendor

contract purchasing, master data management, data governance, data scrubbing,

pricing, and corporate actions. This function should have a reference data

management and cleansing platform to maintain the product and price masters.

There are many drivers for central data management and systemic risk regulations

are confirmation for the need to eliminate data silos and distributed data

management and firms will be under duress to centralize their data management

framework. Internally there may be parochial holdouts where a business group

feels (perhaps correctly) that a central data manager cannot understands their

unique data needs. Why hand over control to a central data factory if the quality of

some of your data and prices declines as a result? New architectures are available

to create a hybrid model which allows for a centralized framework with some of the

data ownership being pushed down to independent groups. They will have an easy

interface to set up their own cleansing rules and valuations while still maintaining

overall organizational control and re-distribution of that data across the firm.

2. Use MDM technologies to break down organization silos and enable cross-enterprise

data aggregation and reporting. Use this to create enterprise customer, client, product,

and price masters.

MDM technologies are being incorporated rapidly, for good reason. Previous data

silos that have grown over time are now becoming real inhibitors to a firm acting on

an enterprise basis. Systemic risk is another driver, but even without mollifying the

risk manager’s concerns, there is great ROI for firms who are able to bring together

data silos into an organizational view.

The key tenets of best practice are multiple and independent sourcing, consistent

conflict resolution, decision transparency and consistent usage throughout the

enterprise. A centralized reference data architecture will lend itself much more easily

to these. Some firms may prefer to augment the centralized management of

reference data with valuations controlled by each product business, using a federated

data management.

As a next step towards data

management, firms should

consider enacting a centralized

data management function, using

MDM technologies and managing

data vendor contracts on a

centralized basis.

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MDM can also help with likely changes to data attribute definitions by pushing out new

data vendor field definitions without requiring major application changes, as well as

cross-organizational regulatory reporting.

3. Manage data vendor contracts on a centralized basis. Work with those vendors to

understand their plans for redistributing utility sourced legal entity and product data as

well as how they will assist the attribute and identifier translation requirement. Data

vendors are also in a position to assist their clients in attribute and identifier translation.

Data vendors are changing the way they contract data. The best practice now is to

manage all data contracts centrally, so that any business unit requiring data has to

come through a central clearing house. Through this the firm can see if the data is

already purchased and cleansed. If new data is required it can negotiate more

effectively with the vendor, cleanse it and make it available across the firm in a

uniform consistent manner. Contractual renegotiation can be on an ‘all you can eat’

basis. It is clear that if organizational hurdles can be overcome that this approach will

have great ROI.

Counterparty data management should be examined to ensure the efficient

maintenance and processing of accurate data and to look at opportunities to improve

the trade and compliance processes and determine if greater confidence be can given

to its data quality and cross-enterprise availability.

Counterparty data will be changing and regulators will demand the use of new

identifiers. This requires firms to maintain parallel mappings of old and new identifiers

or to migrate to new ones. Data quality is also likely to improve and firms should

examine if they should be maintaining their own counterparty data. If they are, there is

a good chance that there are big savings to be made migrating to a data vendor who

can also maintain those counterparty mappings.


CONCLUSION

No one knows exactly what systemic risk regulatory analysis and reporting changes will

be mandated by the new regulatory bodies: the FSOC and the OFR. We do know that

the timescales are aggressive, iterative waves of requirements are likely, and firms will

have little time to react. There are preparatory changes the industry can make now to

enable it to react faster once the details are known and to secure a greater ROI.

Contact: [email protected]

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RESOURCES• Summary of the Dodd-Frank Wall Street Reform and Consumer Protection Act,

Enacted into Law on July 21, 2010. Davis Polk July 2010.

• Technical Capabilities Necessary for Regulation of Systemic Financial Risk –

Summary of a Workshop. National Research Council of the National Academies.

• Systemic Risk Information Report. SIFMA Deloitte, June 2010.

• Department of the Treasury, Office of Financial Research: Statement on Legal Entity

Identification for Financial Contracts, November 2010.

ABOUT THE AUTHORPhilip Filleul is Solutions Manager for Patni’s Reference Data Solution. He has 24 years

experience in financial systems having held senior positions with major suppliers to large

banks including IBM and Sun Microsystems, focusing in the last few years on risk,

compliance, and reference data.

ABOUT PATNIPatni Computer Systems Ltd. is one of the leading global providers of Information

Technology services and business solutions. Around 16,000 professionals service clients

across diverse industries, from 28 international offices across the Americas, Europe and

Asia-Pacific, and 23 Global Delivery Centers in strategic locations across the world. They

have serviced more than 400 FORTUNE 1000 companies, for over three decades.

Patni has reference data management practice expertise in optimizing data architectures,

expertise in implementing market leading reference data management platforms, and a

business process outsourcing group currently delivering manual reference data cleansing

services to a number of major organizations.

patni wp data management implications of forthcoming systemic risk regulations

Economy & Finance

financial data

financial risk management

financial institutions

financial regulation

financial reporting

nonbank financial companies

financial stabilityin

risk management standards