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.

Important new agencies
established under the
Dodd-Frank Act are Financial
Stability Oversight Council
(FSOC) and the Office of
Financial Research (OFR).

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.

DATA MANAGEMENT IMPLICATIONS OF FORTHCOMING SYSTEMIC RISK REGULATIONS 02

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.


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.

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 SUMMARY
In 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 NEED FOR A COMMON LANGUAGE FOR SECURITIES
AND ENTITIES
The 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
MONITOR
Systemic 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.


MONITORING NETWORKS OF COUNTERPARTY RISK
EXPOSURES
Firm 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 TOOLS
The 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 SUMMARY
The 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.


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.

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.

CONCLUSIONS FROM THE NAS AND SIFMA ANALYSES
The 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.


• 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.


• 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 DATA
For 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.


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 DATA
Most 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 COOPERATION
The 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.


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.

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.


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 TAGGING
It 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 REPORTING
It 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 ANALYSES
Based 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 TRANSPARENCY
Any 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.


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.

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.


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.


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.

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