WHITE PAPERDATA MANAGEMENT IMPLICATIONS OFFORTHCOMING SYSTEMIC RISK REGULATIONS              The Dodd-Frank Wall Street Re...
Important new agenciesestablished under theDodd-Frank Act are FinancialStability Oversight Council(FSOC) and the Office of...
According to the DFA, the OFR will establish the standards for financial reporting andimprove the quality of data it recei...
The National Academy ofSciences (NAS) Report and theSIFMA Report provide a greatinsight into the systemic riskanalyses lik...
THE NEED FOR A COMMON LANGUAGE FOR SECURITIESAND ENTITIESThe systemic risk regulator will have to unambiguously aggregate ...
MONITORING                NETWORKS              OF       COUNTERPARTY                   RISKEXPOSURESFirm specific data in...
3. Aggregated risk reporting templates — Similar to a global Chief Risk Officer, the     regulator would develop a consist...
• Since systemic risk analysis is a new discipline we should expect research to be funded on new analytic techniques. Thes...
• While not in the eight approaches summarized above, the NAS and SIFMA reports reiterated the need for data comparability...
Under the auspices of the Enterprise Data Management Council, the industry has beenworking on a precise semantic definitio...
Action items directly affecting                                                                                           ...
Higher quality entity data should reduce the need for large investments in resources forentity data scrubbing for sell-sid...
The key tenets of best practice are multiple and independent sourcing, consistentconflict resolution, decision transparenc...
MDM can also help with likely changes to data attribute definitions by pushing out new  data vendor field definitions with...
RESOURCES                                      • Summary of the Dodd-Frank Wall Street Reform and Consumer Protection Act,...
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Patni wp data management implications of forthcoming systemic risk regulations

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Patni wp data management implications of forthcoming systemic risk regulations

  1. 1. WHITE PAPERDATA MANAGEMENT IMPLICATIONS OFFORTHCOMING 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.
  2. 2. Important new agenciesestablished under theDodd-Frank Act are FinancialStability Oversight Council(FSOC) and the Office ofFinancial 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
  3. 3. According to the DFA, the OFR will establish the standards for financial reporting andimprove the quality of data it receives from buy-side and sell-side firms and fromsystemically important bank and nonbank entities. The OFR will include a Researchand Analysis Center to provide analytics and tools necessary to monitor systemic riskin 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 theanalytics 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 stabilityIn addition to these responsibilities, if the OFR’s analysis deems it necessary, it canrecommend to the FSOC and Congress “heightened prudential standards” regarding: • Risk-based capital • Leverage • Liquidity • Contingent capital • Concentration limits • Enhanced public disclosure • Overall risk managementWhile the industry waits for the OFR to provide rules and guidance on its reportingrequirements, what initiatives can buy-side and sell-side firms be undertaking nowto prepare?The remainder of this paper offers some insights, suggestions, and practical advice. Withthe aggressive timescales likely to be required by the OFR, if firms wait for the details tounfold before taking preemptive actions, they may be unable to respond in time. DATA MANAGEMENT IMPLICATIONS OF FORTHCOMING SYSTEMIC RISK REGULATIONS 03
  4. 4. The National Academy ofSciences (NAS) Report and theSIFMA Report provide a greatinsight into the systemic riskanalyses likely to be needed andthe data requirements that willflow from them. They are anecessary precursor tounderstand how OFR mightapproach 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 DATA MANAGEMENT IMPLICATIONS OF FORTHCOMING SYSTEMIC RISK REGULATIONS 04
  5. 5. THE NEED FOR A COMMON LANGUAGE FOR SECURITIESAND ENTITIESThe systemic risk regulator will have to unambiguously aggregate and interpret eachfirm’s risk data. Today there are a number of structural issues for data that significantlyinhibit this goal. Primary among these is that there is no industry standard non-proprietarycounterparty or legal entity identifier. Each firm either creates their own set of uniqueidentifiers or uses those of their counterparty data provider. A regulator seeking toaggregate counterparty exposures cannot do so without an industry standard entityidentifier. Secondly, there is no standard security classification code or transaction type soa 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 toaggregate risk and counterparty commitments. As such there are no standard ways ofdefining the detailed attributes of complex instruments so each firm takes its ownapproaches and any aggregation to a regulator would have to resolve inconsistencies.THE DATA NEEDED FOR SYSTEMIC RISK MONITORINGThe regulator will need to make judgments on when certain firms or market segments areover 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, forinstance, 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 tosystemic 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 rangingbetween more is good and more is harder to analyze, the summary being that theanalytic models will drive the data requirements and that those models are not yet builtand will iterate over time. It would be impractical for regulators to return to banks everyfew months with new data requirements as they create new analytic models. It is morelikely that they will initially err on the side of caution and ask for more rather than lessdata. As their models increase in sophistication, the data will be in place.SIGNALS THAT A SYSTEMIC RISK REGULATOR MIGHTMONITORSystemic risk can come from multiple points of origin. Discussions have focused on firms’positions and transactions but there are many potential contributors, such as housingprices 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 aspectssuch as transaction velocity as well as valuation variance between mark to market andvaluation models. As situations become stressed, gross exposures become moreimportant than net exposures as it becomes harder to unwind large short or longpositions as liquidity dries up.The area is extremely difficult to regulate and is likely to see many iterative changes asunderstanding improves. Right now we do not know if we can foretell instability, or even ifwe could, whether corrections could be made in a controlled way, for example to preventherding out of an illiquid crowded trade. DATA MANAGEMENT IMPLICATIONS OF FORTHCOMING SYSTEMIC RISK REGULATIONS 05
  6. 6. MONITORING NETWORKS OF COUNTERPARTY RISKEXPOSURESFirm specific data in isolation does not illuminate all relevant sources of risk. It is alsonecessary to understand the exposures to firms’ counterparties. These counterparty risksexist in a cascade of relationships and are globalized. In order to understand thenecessary systems behavior we need to know how an initial credit event may impactexposures in other firms. We will need more understanding of interconnectedness andhow 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 andfine-grained models are required at both a macro and a micro level. These models wouldhave to adapt to changing networks and topologies. Currently there is no significantanalytic 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 runby a regulator. The 2009 Supervisory Capital analyses (SCAP), was performed by the 20largest firms at the height of the crisis. This approach was generally thought to besuccessful with relatively simplistic like-for-like analyses. It is, however, valuable for theregulator to have the data and analytic capability within its own domain, as it can performanalyses more quickly without signaling its areas of concern. This reinforces the need todevelop analyses with data requirements in advance i.e., creating a super-set of data heldfor analysis by the regulator.SIFMA REPORT SUMMARYThe study was commissioned by SIFMA on behalf of its members and performed byDeloitte LLP and seeks to promote greater awareness and understanding of potentialsystemic risk information requirements. The authors used the NAS report as its startingpoint 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 informationapproach that would serve all the needs of a regulator or firm. Eight key systemic riskinformation approaches were identified. Each has its own advantages and disadvantagesin terms of the net benefit of the approach, its resource requirements in the firm and theregulator, 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. DATA MANAGEMENT IMPLICATIONS OF FORTHCOMING SYSTEMIC RISK REGULATIONS 06
  7. 7. 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 ANALYSESThe following conclusions from the SIFMA and NAS reports are speculative for thepurposes 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. DATA MANAGEMENT IMPLICATIONS OF FORTHCOMING SYSTEMIC RISK REGULATIONS 07
  8. 8. • 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. DATA MANAGEMENT IMPLICATIONS OF FORTHCOMING SYSTEMIC RISK REGULATIONS 08
  9. 9. • 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 numerousshort-term impacts and long-term benefits to the industry. We discuss this in moredetail 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 DATAINITIATIVESWe 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 theAtlantic in the value of a reference data utility. The argument is that reference data fallsinto two main categories: either factual or interpretive. Factual data would be legal entityor securities definition information. Interpretive data would be, for example, securitiesend-of-day pricing and valuation data. It is appropriate to have more than one opinion oninterpretive 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 theutility for product and counterparty factual data.If a utility did provide an authoritative source of legal entity and securities data then itcould be distributed to the existing vendor community who could then repackage anddistributed to their client base. This would retain existing business models and offervendors both the possibility of higher quality data and a potential lower cost base, whilegiving 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 accountantsprecisely describe it in a term sheet. This sheet is used by multiple data vendors whointerpret it and populate a new record for that security. For simple securities the vendorsdo this very well but for complex products it is easy for attributes to be missed ormisinterpreted due to time pressure and human error. Investment firms spendconsiderable effort to cross compare product feeds for a new security and frequentlyintervene manually to resolve differences in opinions on what should be factual data. DATA MANAGEMENT IMPLICATIONS OF FORTHCOMING SYSTEMIC RISK REGULATIONS 09
  10. 10. Under the auspices of the Enterprise Data Management Council, the industry has beenworking on a precise semantic definition of all the attributes necessary to define a securityacross asset classes. The pilot was done in the asset class of Mortgage BackedSecurities (MBS). This effort has been extended across asset classes and is nearly readyfor market introduction.There is a growing consensus to use this semantic definition at the point of issue within areference data utility so that the information defining a security, when those lawyers andaccountants initially describe it, is tagged authoritatively at the point of issue. Thisinformation would then be made available to data vendors, who would easily repackageand distribute it without the intervening step of term sheet interpretation. This would givethe industry an accurate, precisely interpretable and consistent view of product data madeavailable through existing channels.Alternatively, complex attributes could have a different interpretation between the datavendor’s prior definition and the definition employed by the reference data utility. Thiscould require mapping of attributes.COUNTERPARTY DATAMost data vendors employ large staffs cleansing counterparty and entity data. Manyinvestment firms also employ large staffs doing exactly the same thing, representing alarge overhead for the industry, which inevitably creates multiple opinions on the samecore facts. The problem is exacerbated by the lack of a common standard for a uniqueentity identifier. Fixing the identifier issue (described below) could come quickly andshould be incorporated in counterparty data vendor’s feeds. Moving to a common sourceof entity data could take longer but is a valid industry goal.INTERNATIONAL COOPERATIONThe severity and global nature of the financial crisis has produced an unprecedenteddegree of international cooperation. There has been great acceptance of the internationalnature 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 StabilityBoard (FSB) in London in April 2009.“The Financial Stability Board (FSB) is established to coordinate at the international levelthe work of national financial authorities and international standard setting bodies in orderto develop and promote the implementation of effective regulatory, supervisory and otherfinancial sector policies. In collaboration with the international financial institutions, theFSB will address vulnerabilities affecting financial systems in the interest of globalfinancial stability.”Members of the FSB span most countries and include their central bank and bankingsupervisory regulator plus the main international standards setting bodies and financialinstitutions such as the BIS, the World Bank, and the IMF. Europe has established theEuropean Systemic Risk Board (ESRB) with a role similar to the OFR in the US. Whilethese organizations are just being formed, we can expect aggressive build outs and greatcooperation on data sharing and standards.Both sides of the Atlantic are discussing the potential role of a reference data utility, as itwould make most sense to have this created on an international basis if the cooperationcan be effectively achieved. DATA MANAGEMENT IMPLICATIONS OF FORTHCOMING SYSTEMIC RISK REGULATIONS 10
  11. 11. 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 DATAMANAGEMENTWhile there are many potential impacts on financial firms, we focus on those that wethink 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 directlywith 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 transparencyEach of these line items is examined in further detail below with recommendations onhow to prepare for them.NEW ENTITY IDENTIFICATION STANDARDSThe SEC is drafting a derivatives transparency rule for mid 2011, which is driving theneed 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) withthe objective of creating a “universal standard for identifying parties to financialcontracts”.However, until the standards are set and implemented, the entity identifiers used withinfirms will not be ‘standard’. Both buy-side and sell-side firms will most likely requiremodifications to applications or will adopt means to translate from external to internalidentifiers allowing applications to run unmodified. Firms such as Avox already providethis mapping service between internal and external identifiers.The Know-Your-Customer (KYC) function can maintain this master counterparty data as newcounterparties are on-boarded and compared with the counterparty data provider’s recordsas soon as possible in the trading process to ensure consistency. DATA MANAGEMENT IMPLICATIONS OF FORTHCOMING SYSTEMIC RISK REGULATIONS 11
  12. 12. Higher quality entity data should reduce the need for large investments in resources forentity data scrubbing for sell-side firms. Buy-side firms should also consider takingadvantage of this data provision by looking at new functionality such as pre-trade analysisof 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 notbe immediate, but when it happens it will improve the consistency of product data andagain should be distributed by existing reference data providers. As with entity id, thesemantics of individual attributes are likely to be modified and standardized.Organizations should work with existing reference data providers when there is moreclarity on how they will carry the old and new attribute definitions. It is also quite possiblethat commercial utilities will provide translation services.Master Data Management (MDM) technology is discussed in this paper as a valuableapproach for providing easier compliance and many other direct business benefits. MDMcan also provide translation of field attributes to consuming applications, which can thenrun unmodified.MULTI ASSET CLASS REPORTINGIt is likely that the OFR at some point will require transaction and position reporting acrossasset classes including OTC derivatives. The shift to derivative CCP’s backed by assetclass trade repositories means that these repositories will have the bulk of transactionsand positions in many asset classes, so in theory they should be able to perform the OFRreporting responsibility thereby offloading firms. There is a strong expectation thatcommercial utilities will also provide reporting applications that will take firms’ input in theirown 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 theheight of the crisis, it is likely that standard analyses will need to be provided to the OFRon a regular basis. SIFIs will likely also be subject to additional reporting. The types ofreports will probably include counterparty exposure, risk concentrations, average liquidityetc., and the analyses will be enterprise wide. If firms maintain business silos withoutcentralized data governance and technology designed to integrate data between silosthey will be at a significant disadvantage.MDM technologies can be used as a way of bringing silos of data together into acentralized virtual data model. MDM can apply business rules to merge duplicated dataacross data silos and resolve any data interpretation inconsistencies. This process movesenterprise reference data towards a single logical data model, which assists both theregulatory analysis and reporting needs.PRICING TRANSPARENCYAny reference data utility is unlikely to be a provider of asset prices as these arefundamentally interpretive, so a multiplicity of opinion is desirable. This will leave theindustry infrastructure largely unchanged as regards pricing. However, investors arealready stepping up the pressure to provide an independent, sophisticated andtransparent approach to pricing. There is a large industry body of knowledge in this area,so we do not discuss it further. DATA MANAGEMENT IMPLICATIONS OF FORTHCOMING SYSTEMIC RISK REGULATIONS 12
  13. 13. The key tenets of best practice are multiple and independent sourcing, consistentconflict resolution, decision transparency and consistent usage throughout theenterprise. A centralized reference data architecture will lend itself much more easilyto these. Some firms may prefer to augment the centralized management ofreference data with valuations controlled by each product business, using a federateddata 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 NOWIncorporating 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. DATA MANAGEMENT IMPLICATIONS OF FORTHCOMING SYSTEMIC RISK REGULATIONS 13
  14. 14. 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.CONCLUSIONNo one knows exactly what systemic risk regulatory analysis and reporting changes willbe mandated by the new regulatory bodies: the FSOC and the OFR. We do know thatthe timescales are aggressive, iterative waves of requirements are likely, and firms willhave little time to react. There are preparatory changes the industry can make now toenable it to react faster once the details are known and to secure a greater ROI. DATA MANAGEMENT IMPLICATIONS OF FORTHCOMING SYSTEMIC RISK REGULATIONS 14
  15. 15. 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.REGIONAL HEADQUARTERS ABOUT THE AUTHOR © 2010 Patni. All rights reserved. All brand names and trademarks belong to their respective owners. Philip Filleul is Solutions Manager for Patni’s Reference Data Solution. He has 24 yearsAMERICASUnited States experience in financial systems having held senior positions with major suppliers to largePatni Americas, Inc. banks including IBM and Sun Microsystems, focusing in the last few years on risk,One BroadwayCambridge, MA 02142. compliance, and reference data.Tel: +1 617-914-8000Fax: +1 617-914-8200EMEA ABOUT PATNIUnited Kingdom Patni Computer Systems Ltd. is one of the leading global providers of InformationPatni Computer Systems (UK) Ltd. Technology services and business solutions. Around 16,000 professionals service clientsThe Patni Building264-270, Bath Road across diverse industries, from 28 international offices across the Americas, Europe andHeathrow UB3 5JJ. Asia-Pacific, and 23 Global Delivery Centers in strategic locations across the world. TheyTel: +44 20 8283 2300Fax: +44 20 8759 9501 have serviced more than 400 FORTUNE 1000 companies, for over three decades.SAARC Patni has reference data management practice expertise in optimizing data architectures,IndiaPatni Computer Systems Ltd expertise in implementing market leading reference data management platforms, and aAckruti, MIDC Cross Road No 21 business process outsourcing group currently delivering manual reference data cleansingAndheri (E), Mumbai 400 093.Tel: +91 22 6693 0500 services to a number of major organizations.Fax: +91 22 6693 0211APACSingaporePatni (Singapore) Pte Ltd61 Robinson Road D-003_041210#16-02 Robinson CentreSingapore 068893.Tel: +65-6602-6600Fax: +65-6602-6610Contact: bfs@patni.com www.patni.com 25+ years in IT Services | 16000+ employees | SEI-CMMI-Dev Level 5 (V 1.2) | ISO 9001:2008

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