The resetting of the risk management agenda through successive capital accords has had little impact on the ability of many firms to prevent losses. This raises concerns as to a possible disconnect between the risk calculation methods applied in the calibration of regulatory capital and the risk exposure measurement and management systems implemented by firms. These concerns are further compounded by the continued absence of a standardized dynamic, aggregatable and replicable risk exposure measurement framework.

This presentation addresses these concerns by introducing a risk accounting solution based on a new risk metric abstraction, the Risk Unit. Risk accounting is designed to risk-weight the notional values of transactions destined for posting to the accounting records of the firm. In this way, it provides empirical means of directly tying the accounting records and related financial performance metrics to dynamic measurements of exposure to risk expressed in Risk Units.

The direct alignment of financial metrics and risk exposure metrics enables the risk appetite setting process to become an integral part of the financial planning and budgeting cycle. Over time risk exposure metrics can be correlated to expected and actual losses thereby imparting a monetary value to the Risk Unit abstraction, and can be used to risk-adjust betas in Capital Asset Pricing Models’ expected return assumptions thus bridging economic theory with risk management concepts.

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The rest of the world, however, will be taught how mathematics mixed with a perverse incentive compensation system nearly collapsed the global economy.

What is least apparent, however, is that the start of fixing the problem, illuminating the complexity, starts with data “transparency.” Complete “transparency” into the basis of financial transactions is possible now because of the capability of computers to let their human users peer into the underlying details of products traded and the counterparties trading them.

Read the entire article here.

Read the article at chiefexecutive.net

A little known, almost obscure provision in the Dodd-Frank legislation, the establishment of the Office of Financial Research (OFR) under the US Treasury is set to impose yet another identification system on corporations. Although many corporate executives might think it is just a financial institution’s issue, the OFR is set to have a profound effect on all businesses that originate and otherwise participate in financial markets.

After weathering the 2008 global financial crisis, many corporate CEOs realized that preventing another financial meltdown is as much their challenge as Wall Street’s. In this past crisis, corporations’ shareholders were disenfranchised, businesses couldn’t get loans from banks, public companies were neither able to raise capital nor issue commercial paper, and business users of hedging markets were asked to put up huge amounts of additional collateral or cash margin.

What became clear during and after the Lehman collapse, is that regulators had no way to assess overall financial system or counterparty risk. There was no universal identification system for the financial services industry and hence no overall visibility of financial participants and products.

This is quite amazing when you consider the information age we are in, the vast sums of money on the table and the fact that other global industries like retail, food, consumer, healthcare and manufacturing have been using unique manufacturer and product identifiers in barcodes for years.

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May 17, 2010

Prof. Mark J. Roe argues that clearinghouses may not be adequate to prevent default
on derivatives or may put taxpayers at risk (“Derivatives Clearinghouses Are No Magic
Bullet,” op-ed, May 6).

Clearinghouses require minimum capital to be posted for each transaction at the initial
acceptance of a trade, then mark-to-market the positions daily, even intraday, and in
volatile markets more often than that. The daily settlement of these trades allows for the
earliest warning of failure to pay with enough capital in reserve at the clearinghouse,
built up by multiple margin calls throughout the life of the contract to buttress a minimum
number of days of the severest market declines. When the clearinghouse declares a
member firm overdue on its daily settlement commitments, the defaulter’s positions can
be transferred to another willing clearing member’s account.

Those positions are trades originally entered into between counterparties of a defaulting
member, and whose obligation to pay was transferred to the clearing house as the
central counterparty.

The speedy transfer of positions of the collapsed Refco and Bear Stearns without loss
to clients and clearing members is a testament to the success of this method of riskmanaging
contract markets. The industry comes together to share risk in a
clearinghouse which is better than placing the burden on the taxpayer.

Allan D. Grody
New York
Printed in The Wall Street Journal, page A20

Systemic risk, the latest buzz word to explain the pandemic that caused the financial crisis, is the risk of a system-wide financial crisis caused by the contemporaneous failure of a substantial number of financial institutions, financial markets or both.

In preparation for identifying issues in the Senate’s bill, the Senate Banking Subcommittee on Security and International Trade and Finance heard late last year testimony from an assortment of Ph.D.’s, including Nobel laureates, on how to monitor systemic risk . They concluded that the solution would not be found in an elegant mathematical model nor an enlightening theoretical construct. It would come as a result of getting the data right from the start. It is ironic that Nobel laureates and Ph.Ds are now telling senators that the data identifiers that underpin all financial transactions are broken, and that nothing short of retooling the plumbing will fix it.

Sen. Dodd’s bill calls for the government to set the standard identifiers for companies and instruments in the U.S. financial industry, and maintain it in a reference data base. These identifiers would be used by financial institutions to report their financial transactions and positions to the Data Center.

The idea of gathering quality data for regulators so they can analyze systemic risk is simple to understand, but a very complex procedure to implement. There is no widespread standard for the electronic codes that can be used to identify products and counterparties. There are no universally-accepted identification standards that define the particular terms and conditions of a transaction. If the data is not defined correctly (or consistently), then it leads to huge infrastructure costs and systemic risk. That is what prevented the defaulting pooled mortgages in the collateralized mortgage securities (CMOs), or in the collateralized pools of debt instruments (CDOs) containing these pooled mortgages, not to be traceable nor identifiable. They were incapable of being placed on any regulators’ radar screen.

The current set of systemic failures brings to light the back office paper crisis of Wall Street in the late 1960’s, where the plumbing was first identified as leaky. We thought we had fixed the problem. However, the problems transcended each sovereign regulator or each separately constructed financial market, as firms increasingly transacted business globally and constructed products across financial markets.

The lack of standards results in performing duplicative functions in an attempt to represent each unique product, business entity and valuation price identically. These include duplications of such fundamental identifiers as symbols for corporate issuers and contract markets; numbering conventions for securities; supply chain business entity identifiers; and counterparty identifiers.

A vast infrastructure of payment, clearing  and settlement facilities is needed to match transactional data from origin to payment in order to control the data errors introduced from separately-sourced multiple intermediaries. This delays the process and requires each financial institution to support expensive computerized and people intensive mapping and reconciliation activities. For example, in the U.S. securities markets there is a built-in delay of three days for this process to run its course.

Former U.S. Treasury Secretary Henry Paulson identified payment and settlement systems, which are at the root of the intertwined financial global system, as one of the more important systemic risks that lack coordinated regulation. The Group of Thirty’s (G-30) Final Monitoring report on the Global Payments and Settlement System identified the problem as the failure to have a global collaboration amongst financial institutions to arbitrate and distribute standardized product and business entity identifiers and other referential data.

Most industries have invested in universal product and supply chain identification coding systems to uniquely identify their physical products, transportation intermediaries and counterparties. This helped regulators track a tainted Tylenol capsule back to its manufacturing process and find the source of tainted cows’ meat across the globe. However, financial regulators could not find the mortgage that was defaulted on in a U.S. city that wound up as a toxic asset on the balance sheet of a failing bank in Australia. Financial regulators could not see the counterparty positions allegedly held by convicted financial con artist Bernard Madoff at a London OTC options dealer. And they certainly missed the numerous movements of securities bundled into Lehman’s Repo 105 collateral moving from the U.S. to the U.K. and back again to dress up their US leverage ratio.

We don’t need another silo-based government-run data center nor the imposition of government built data standards. The financial industry needs to self administer a coordinated global standard under regulatory oversight by the G-20’s Financial Stability Board, already given the authority and responsibility to oversee systemic risk globally. No financial firm could issue a new product without getting a universal code first. Look at any bar coded product in any supermarket across the world and you’ll see the simplicity of an industry driven solution.

Allan D. Grody is President of Financial InterGroup Advisors and a Founding Board Member of the Journal of Risk Management in Financial Institutions. Dr. Robert M. Mark is the Managing Partner at Black Diamond Risk Enterprises.

The current state of risk management is as if we sit below a volcano, knowing the probability of the volcano exploding and a decent guess as to the devastation it would cause. But don’t we really want to sit next to a seismometer and continually measure the increasing volcanic activity, perhaps evacuate before it explodes?

Regulators expected that the provisioning of capital for extreme losses would sustain financial enterprises in periods of stress. In the current mindset of this financial crisis, perhaps a more appropriate view of these capitalmeasures is as the ruler by which an organization counts down to failure, not the system that proactively prevents it. Did regulators truly believe these capital rules would prevent financial institutions from failing?

To be fair to regulators, they did expect to see the coincident evolution of a risk culture within these institutions along with the development of a risk exposure measurement system to capture key operatingmetrics that could affect its operational risk profile. Taken together, and with regulatory oversight, it was anticipated that the new risk regime would do just that—prevent failures, or at least give an early warning of pending doom.

However,whether by abdication or by push back fromthe industry, or simply because there was not sufficient time to evolve in a natural way, we stopped the riskmanagement process at capital provisioning. And we certainly failed in risk oversight.We need to geton with risk measurement— along with capital measurement and more rigorous oversight—so we can manage risk.

Risk management has always been an intuitive management skill that was and is expected of all business managers. Business managers manage their revenue and costs through performance management systems. They manage their risk through analysis of various operating metrics and measure the impact fromtheir experience and judgment. The problem with this approach is that it lacks the ability to be measured and aggregated in any systematic way. It is left to a wide range of relatively subjective analyses performed by internal and external auditors around Sarbanes-Oxley; the Committee on Sponsoring Organizations (COSO) reviews; annual financial audits; cost analysis teams performing unit costing; business process reengineering and Six Sigma exercises; and by risk managers applying scorecards and risk control self assessments.

It was and still is wrongheaded to believe that a historical,mathematically modeled view of past losses, manifest in capital provisioning, would prevent too much risk from being taken. Financial transactions that are entered into in real time have the potential of risk exposures cascading far beyond their notional values and certainly far beyond capital provisioned frompast loss events.

The industry has not yet found a way to identify operational exposures and put a consistent and comparable value on them. Operational risk, in all its diversity and complexity, is thought not measurable. In the absence of such a direct exposure measurement metric the industry has looked to loss history as being the only objective source of information on operational risks. So what would be an approach to observing the risk of loss in an operating environment?

Contrary to conventional thinking, operational risk can be measured. Just look at all the diversity in the human condition represented in a FICO score for measuring retail credit or the diversity of corporate cultures distilled down into credit rating categories, or the complexity of trading strategies across multiple geographies and products synthesized into a market value-atrisk calculation.

An answer to measuring operational risk is found in the evolution of FICO scores and credit ratings. Credit reporting was bornmore than 100 years ago, when small retailmerchants banded together to trade financial information about their customers. Lenders eventually began to standardize how they made credit decisions by using a point systemthat scored the different variables on a consumer’s credit report. Credit granting took a huge leap forward when statistical models were built that considered numerous variables and combinations of variables around these point systems. Today, credit analysis uses a well-defined set of inputs from the historical set of key risk indicators accumulated from many years of refining intuition into predictors of loss.

If we move over to the commercial side of credit ratings we get a similar history and methodology from the major credit rating agencies. Their methods, also refined over a century, associate commercial credit scores into A-B-C rating systems where, for example, a confidence level between 99.96 and 99.98 percent has been calibrated as equivalent to the insolvency rate expected for an AA credit rating.

We start to solve the problem of determining such ametric formeasuring operational risk of loss by returning to the roots of the operational risk capital charge, this being themeasure of the potential for losses derived from processing transactions, for truly that is what financial institutions, in themain, do.We then make the observation that all operational processes in a financial institution are driven by transactions interacting with human, automated and data-dependent activities. Thereafter we dissect each of these pillars into a finite number of subcomponents of standardized activities that reflect key risk indicators that are known intuitively by business managers to cause losses (see “Examples of Mapping Causes of Losses” flow chart, p. 53).

This is a critical observation in that each of these “pillars” of activities represents actionable elements in a transactional process. This is important if risk measurement systems are to be able to support management decisions to mitigate risk before they become losses and capital charges.

We then map transactions categorized by product type to standardized risk-weighted activity, risk weight each of their categories and subcomponents using standardized scales and best-practice optimized weightings (see “Example of a Risk-Weighted Products/Transactions Matrix,” below); and then tie the transaction process to a scaled measure of the financial values associated with each transaction (see “Transaction Value BandWeighting Table,” p. 55).

We perform this analysis by using the enterprise’s personnel and documentation in a structured process that allows first for the understanding of the exposures inherent in the operating environment in which the business exists and translating this knowledge into risk weights.We then use these values for the calculation of a forward looking measure of risk exposure, a scaled inherent risk value, and a risk-mitigating best-practice control value. A set of standardized riskmetrics is then calculated representing inherent risk, riskmitigation effectiveness and residual risk (see “Example of Calculated Risk ExposureMeasures,” p.55).

These risk metrics, applied at the transaction level, can then be aggregated to provide departmental, divisional, subsidiary and groupwide views, and views by categories (i.e., product, geography, business unit and risk type).

This method of calculating risk exposure provides a view of residual risk that is dynamically updated when changes in causal factors occur. In this way the potential for statistical correlation ofmeasurements of exposure to risk and loss history is created which, over time, will cause the risk metrics generated through this new method to become inherently predictive. This is quite different from, but complementary to, the backward- looking capital calculations that financial institutions rely upon today in order to gauge the largest unexpected loss that may occur within a given confidence level and time horizon.

More importantly, it is built from the ground up, allowing for the intellectual property of operatingmanagement to be imbedded in the very fabric of the riskmeasurement system. Institutionalizing such knowledge into the operational risk activity creates credibility and actionability—most critical components in enabling a risk culture to evolve and continual risk mitigation to be its outcome. Without ameasure of risk exposure, and a dynamic mechanism for seeing it build up, we cannot take preventive actions.Without it we will forever be destined to sit below a volcano of impending financial crisis and potential collapse, not to be forewarned of the increasing pressure building up so that we can mitigate the consequences of an explosion.

Collaboration and risk sharing through industry-sponsored centralised facilities, ie clearing houses, central securities depositories, payment and netting systems, and central counterparties, has been used extensively in the financial services industry. However, it has only been applied to the variable value portion of transactions (principally quantities, transaction prices and monetary values). This paper proposes the same techniques be applied, separately and earlier in a financial transaction’s life cycle, to the matching and settling of the non-valued (referential) data components of these transactions. These data components comprise upwards of 70 per cent of a financial transaction’s assembled parts. Faults in properly identifying this data, as well as mismatches that occur in separately sourced and assembled financial transactions that counterparties attempt to match prior to payment, are a large source of matching errors. The result is significant imbedded industry-wide operational costs, individual firm monetary loss and counterparty risk, and global systemic risk.
This paper advises that a new facility, the Central Counterparty for Data Management (CCDM) be established to access and match multiple incoming sources of referential data at the pre-trade financial transaction assembly point, ‘clear’ this data through best-of-breed computer analysis, and ‘settle’ (distribute) industry accepted, CCDM-assured data sets to participants, making it available to be attached to the variable, value portion of the transaction at transaction assembly time. By doing so at the immediate front-end of a financial transaction’s life cycle, significant improvements can be made in lowering operational costs, reducing transaction failure rates, and reducing operational losses resulting from data failures in the post-trade matching, clearing, settlement and reporting environment.

INTRODUCTION

Members of the global financial services industry have evolved a best-practice mechanism of independently and individually sourcing, storing, and distributing relatively static reference data components of a financial transaction. Reference data identifies the transaction as a specific product bought or owned by a specific business entity. It further identifies any subsequent modifications or interactions with various supply chain participants during the transaction’s life cycle. Acquiring, maintaining and managing such data is costly, with faulty data being at the core of significant components of operational losses. This is due, in part, to the non-standardised identification of products, business entities, supply chain participants and other data attributes that should be identical but are not. Industry members acquire multiple versions of reference data from their own sources as well as from commercial data vendors. Each firm chooses its own preferred sets of vendors and its own rules to match what should be identical information, to uncover discrepancies prior to using this data in subsequent downstream business applications and transaction assembly processes.

The industry further observes faulty data by placing the detection and correction of the matching of both sides of a financial transaction at the point where financial transactions between counterparties have to be paid for and/or settled, thus increasing monetary risk from failed matches. The referential portion of the transaction, although available earlier in the transaction’s life cycle, is matched at the same time as the value portion. While reference data is not value-bearing, computer matching algorithms do not differentiate reference data from other data and mismatches and failed transactions occur without regard to the data elements’ business intent.

To date, techniques of mutualised risk sharing through industry sponsored centralised facilities, ie clearing houses, central securities depositories, payment and netting systems, and central counterparties, have been used extensively in the financial services industry. However, it has only been applied to the variable value portion of transactions (principally quantities, transaction prices and currency values). This paper proposes the same techniques be applied, separately and earlier in a financial transaction’s life cycle, to the matching and settling of the non-valued (referential) data components of these transactions.

It is proposed that a new facility, the Central Counterparty for Data Management (CCDM) be established to access and match multiple incoming sources of referential data at the pre-trade financial transaction assembly point, ‘clear’ this data through best-of-breed computer analysis, and ‘settle’ (distribute) industry accepted, CCDM-assured data sets to participants, making it available to be attached to the variable, value portion of the transaction at transaction assembly time. By doing so at the immediate front-end of a financial transaction’s life cycle, the failure rates and losses in the post-trade matching, clearing, settlement and reporting environment can be improved significantly.

BACKGROUND

The recent stress in financial markets has called into question some fundamental infrastructure components of the global payment, clearance and settlement system that are relied upon to mitigate systemic risks. Regulators across the world including The Basel Committee,¹ the US Treasury,^2,3 the UK’s Financial Services Authority,⁴ The European Central Bank,⁵ the US President’s Working Group on Financial Markets,⁶ and the Group of 20⁷ are reviewing the regulation of payment and settlement systems with a view to understanding why they failed to mitigate the risks of complex counterparty interactions across the global capital, contract, cash and currency markets. The consequence of this global review may well be a new regulatory regime and new institutions to further strengthen oversight and reduce systemic risk.⁸

Of significance in all financial transactions in today’s highly automated financial markets is the priority that the data elements of a financial transaction be accurate throughout the transaction life cycle. The ability to both externally match and internally manage financial, business and risk performance data, depends on its accuracy.

Where financial transactions are to be matched externally to a counterparty it must be verified as accurate by matching both sides of a transaction to each other. When mismatches occur on any of the critical data elements, the transaction is cycled back to its originators at the most immediate previous stage for correction and re-submission. Not only does this delay the transaction, it also causes unnecessary repair work and greatly increases costs. When the transaction fails to settle, it creates a loss of money to both original counterparties. The seller has paid their client for the full value of the transaction when they have not themselves received any payment, and the purchaser must borrow against the collateral and pay for, then deposit it into their client’s account, having not received the collateral themselves from the seller. This undermines the trust between counterparties that drive markets.

One of the most intractable and long-standing impediments to global straight-through-processing initiatives, systemic risk mitigation, and further operational efficiencies in the global clearance and settlement system has been the proprietary and non-standard nature of data. In its most basic form, the financial industry is all about electronic data in its many forms, having removed the physically represented forms of financial instruments long ago, in favour of complete data representation — data content as in product and business entity identifiers, message protocols for data transmission, message formats for alignment of data into identifiable business-related transactions, and of late, data tags for computerised access to data attributes within a financial transaction. Standardising this data has long been desired so that industry participants can converse in a seamless, error free and electronic manner with both its counterparties and its regulators in the industry’s long sought after Straight- Through-Processing (STP) vision.

Although the benefit of open and uniform standards is well researched and advocated by many,9–12 it still remains a distant vision because of short-term cost considerations and the expected selfinterests of those vendors, servicing organisations and financial enterprises that have evolved their businesses around proprietary codes and non-standard conventions. These actions taken over many years have placed a high hurdle on clients in switching costs to move to open and uniform standards.^9,10

Now more transparency and the recognition of individual interests preventing the mitigation of systemic risk across interconnected components of the financial system is taking centre stage in the public debate on the causes of the financial crisis.^11,12 Attention is again returning to the interconnected nature of financial firms within the global payment and settlement systems and the role one class of non-standard data, reference data, plays in adding unnecessary risk and costs to each financial firm as well as to the financial system overall.

Reference data is a broad term understood by operating management, information technology professionals, and risk managers alike. Unfortunately each group understands it differently. To the information professional reference data is ‘any kind of data used solely to categorise other data found in a database or solely for relating data in a database to information beyond the boundaries of an enterprise’.¹³ To the risk manager it is ‘internal and external (third party) data that is used to establish the underlying criteria from which credit risk analysis is performed and credit risk exposure is modelled’.¹⁴ To operating management, referential data is information that enables financial transactions to be identified and processed and financial information to be internally and externally reported. It is no wonder that risk management, operating management and information technology professionals are not focused on observing, let alone resolving, one of the most significant operational risks — that of faulty referential data.

This paper tackles this issue with suggestions for a permanent solution to the proprietary and non-standard nature of financial industry referential data using concepts that traditionally have been applied to centralised risk mitigating facilities in Settlement Systems and Central Counterparties.¹⁵ To date mutualised risk sharing within clearance and settlement systems has only been applied to the value portion of transactions (principally quantities, transaction prices and currency values). These same techniques, however, can be applied to the matching and settling of the referential data components of these transactions. Referential data is at the heart of the globally intertwined clearance and settlement system.¹⁶ While reference data is not value-bearing, computer matching algorithms do not differentiate reference data from other data and mismatches and failed transactions occur regardless of the data elements’ business intent. Acquiring, maintaining and managing such data is costly, estimated now at US$1bn+ annually for each of the largest financial enterprises, with faulty data being at the core of significant components of operational losses.¹⁷ DTCC estimates that 5 per cent of secondary market trades fail to settle each day. With approximately US$7.5trn of settlement value in 2007 at DTCC alone, failed transactions due to faulty data results in total lost interest per day for the industry of US$4bn. This paper suggests that a new facility, the CCDM, be established to acquire and match multiple incoming sources of referential data, clear this data through best-of-breed computer analysis and settle (distribute) industry accepted, CCDMassured data sets to participants and, in turn, to their downstream correspondents. The CCDM would be established, initially, by a handful of industry infrastructure entities and multi-national financial firms as first described by the Group of Thirty,¹⁸ chartered as an exempt clearing corporation with ownership eventually passed to the industry and overseen by regulators.

While most solutions to improve settlement processes focus on post-trading arrangements these recommendations are intended to establish the matching, clearing and settlement of reference data at the pre-trade financial transaction assembly point. By doing so at the immediate front-end of a financial transaction’s life cycle, pre-assembling and ‘certifying’ the reference data elements, the post trade environment can be improved significantly.

The essential hypothesis of this paper is the benefit of a global ‘central’ counterparty (ie one ‘golden copy’) for the industry, vs multiple golden copies, one for each firm (the Enterprise Data Management or EDM model), or multiple ones shared by multiple firms/facilities in multiple outsourced facilities. The industry is already embarked on rationalising content standards such as instrument codes and business entity identifiers and implementing EDM and outsourcing models, and will thus make it much easier to evolve to the final phase, that of applying the central counterparty concept to data management globally. Here, the focus is both on cost efficiencies and risk mitigation, as well as the recognition that the financial industry has become global, transcending sovereign state regulations and, even, regional regulatory compacts.

OPERATIONAL PERSPECTIVE

Financial transactions can be thought of as a set of computer encoded data elements that collectively represent standard reference data, identifying the transaction as a specific product bought by a specific business entity; variable transaction data such as quantity and amount; and other associated referential information such as price data, credit ratings and other types of fundamental data. Analogous to specific component items of a manufactured product, reference data, which comprises 70 per cent of a financial transaction, also defines the products’ changing specifications (periodic or event driven corporate actions), occasional changes to sub-components (calendar data, credit rating, historical price, betas, correlations, volatilities) and seasonal incentives or promotions (dividends, capital distributions and interest payments).

Today’s process of organising a financial transaction from order presentation to trade completion and payment is multiphased. Interactions between different automated business applications, each defining their own unique set of reference data is common. Combined with many points of human and automated system interactions, faulty reference data requires extensive error detection and repair procedures.

Reference data should be consistent across each financial transaction’s life cycle and throughout its supply chain. However, duplication of reference data is pervasive in large financial enterprises and throughout the industry, leading to significantly higher risk and operational costs. When reference data that should be identical are not, it causes miscalculated values, misidentified products, and involvement with erroneous supply chain partners (trade counterparties, custodians, paying agents et al). These individual transaction failures cause monetary loss, higher labour costs, and the potential for both transactional and systemic failure.

The problem, simply stated, is that each financial institution or supply chain participant has independently sourced, stored and applied reference data to their own copies of their multiple inventory and business entity databases. When this reference data is applied to the variable components of a financial transaction (ie quantity and transaction price), and an attempt made to aggregate this information in one financial institution as for reporting purposes, or match, identically, the details sent by a counterparty or supply chain intermediary in order to accept and pay for the transaction, significant failures in matching occur.

One component of this reference data Business Entity Identification (BEI), has become a critical issue of late as regulators focus on know-your-customer (KYC) rules, Anti-Money Laundering laws, the Sarbanes-Oxley (SOX) legislation, the Markets in Financial Instruments Directive (MiFID), the Undertakings for Collective Investments in Transferable Securities (UCITS) III Directive, and the Basel Committee overseeing the Basel II Capital Accords, among others. Here, risk calculations and regulatory reporting using a client’s total holdings, credit profile and credit limits require not only precise BEIs but precise linkage to other BEIs in the overall entities’ ownership structure and hierarchical relationships. Many of these problems map back to the lack of data standards and subsequent data quality issues.¹⁹

The impact in complying with these regulatory mandates is forcing an old issue of incomplete, non-standard electronic representations of customers, corporations, financial intermediaries, issuers, financial products, markets, currencies and prices to the fore-front of management priorities. Whether sourcing this information from vendors such as S&P, D&B or governmental registration authorities, or attempting to reconcile the data within the business silos that make up financial institutions, the data is non-standard and maintained in proprietary formats.

RISK PERSPECTIVE

External examination is now focusing on the dominance of the financial firms’ siloed business models which, in turn, have created siloed implementations of referential databases. These multiple databases have necessitated reconciliations of data across an enterprise to report a single view to regulators at best, and more realistically has caused the reporting of multiple views of the same data from the different business silos and certainly from the many firms that are supposedly reporting on the same set of products or business entities. Recently the Basel Committee on Capital Adequacy was particularly vocal in their ‘condemnation’ of the siloed approach that has historically characterised financial services businesses’ organisational models.²⁰ Could anyone value accumulating exposure to a failing business entity in any timely manner, if at all, as was the recent challenge faced with failing firms such as Bear Sterns, Wachovia, Washington Mutual, Northern Rock and Lehman Brothers (see Figure 1)?

The failure to take a broader view in analysing faulty reference data, defined as a Basel II operational risk for which capital has to be set aside, has led to negative consequences for both market and credit risk capital calculations. For example, historically most organisations have failed to identify a comprehensive well-defined separate ‘operational risk bucket’ to place operational losses in. Many operational losses were most likely identified as either a credit risk (eg a counterparty misidentification, an improper delivery vs payment address, an improper account allocation, etc) or a market risk (eg wrong product identification, missed stock-split date, improper conversion rate, etc). Now, within the new mandate of Basel II, faulty reference data should find its way into the right operational risk bucket. The key, therefore, is to implement an appropriate operational risk management framework that contains a mechanism that can observe causal relationships that drive these operational loss events.²¹

The Basel Committee has stated that financial institutions will be allowed to reduce their capital allocations for operational risk by as much as 20 per cent through risk mitigation. The primary risk mitigate permitted for operational risk currently is insurance. While not specifically making any reference to outsourcing, but certainly embracing it in concept, such ‘risk mitigates other than insurance’ can certainly be construed as an ‘outsourced’ or mutually owned central counterparty for data management as proposed. The risk mitigating and captive insurance structures of such a facility should make it available for capital relief under the Basel criteria.²²

One such entity, the Global Joint Venture Matching Service, now known as Omgeo, was approved by the SEC in 2001 as an exempt clearing corporation to mitigate post-trade risk in the matching and settlement of institutional securities.²³ A similar exempt status could be obtained for an entity formed to match and settle a set of standard reference data for universal use in the pre-trade data assembly process, the valuation and price assembly process, and in assembling data for corporate event activities. Such an entity, organised in collaboration with large financial institutions (say, similar in governance to most other central counterparties) would be a useful vehicle to minimise operational risk for all who subscribe to its standardised data. This would include mitigating the risk of incorrect portfolio valuations, failed transactions, incorrect inventory adjustments, and erroneous dividend and interest payments.

STANDARDS

Much activity is present in the referential standards setting space. Organisations such as the International Standards Organization (ISO), the MiFID Joint Working Group, the SWIFT organization, The Group of Thirty, the ANNA Service Bureau, the International Securities Association for Institutional Trade Communication (ISITC), the Reference Data User Group (RDUG) and its successor organisation ISITC-Europe, The Enterprise Data Management Council, the Financial Information Services Division of the Software and Information Industry Assoc. (SIIA), the Asset Backed Securitization Forum, The UK’s Pension Regulator, the International Society of Securities Administrators (ISSA) and many more ad-hoc working groups, industry task forces, industry trade associations and industry regulators provide forums where asset managers, banks, broker/dealers, hedge funds, global custodians and other financial enterprises and financial institutions discuss issues pertinent to straight-through processing (STP)²⁴ and work towards improving their understanding of solutions that could improve performance for their own firms, as well as devising common solutions to shared problems in the standards and market practices space.

Adding to this complexity is the multiple development activities and varied implementations of securities messaging standards such as Fix Protocol Ltd’s Financial Information Exchange (FIX) and FIXML messaging protocols to standardise formats for communication of pre-trade and trade information, principally equities and fixed income securities and their content between broker/dealers and investment managers; the ISITC schemas for messages between investment managers, broker/dealers and custodians; the FpML schema for derivatives; and the 15022 and 20022 schemas from SWIFT. SWIFT, in conjunction with the ISO’s Working Group 11, is redefining the 15022 standard to incorporate the existing FIX Protocol, the FISD’s MDDL (Market Data Delivery Language) schema (and its recent work incorporating corporate action data into the MDDL schema), into an all encompassing standard known as the UNIversal Financial Industry (UNIFI) message scheme.²⁵

While the initial industry’s focus on data content standards is essential, and a lot more needs to be done, what the industry is now tackling as an intermediate step is a standard taxonomy for tagging of data. The aim is to provide unambiguous nomenclature with precise definitions in the context of real business requirements from which all data sources can be tagged in a standard way for computer-based searching, access and comparison. The precise identification of data elements used by financial institutions should be the foundation of effective data management. Here, such ‘tagging’ of data is at the baseline of many industries. The Internet requires it for precise searching. Manufacturers use it for supply chain management. And retailers use it for more efficient inventory control. But while other industries have made progress, the financial industry still grapples with the problem of terms that have different meanings in different segments of the business, common meanings that use different terminology, and vague definitions that don’t capture critical nuances.

This seems incongruous for an industry that operates real-time trading systems around the clock and around the world, processing nearly half a million transactions per second in the US equities markets alone, for a whole range of complex tradable capital and contract market instruments, and with an increasing mandate of highly automated straight through processing.²⁶ This straight through processing environment is still a distant vision, even though the financial industry has just the information flow to consider. While some progress has been made, financial enterprises are still debating the basics of reference data standards, while seeking the solution to the issue of faulty reference data, both precursors to achieving straight through processing.

The issues dealt with in all these standards initiatives include definition of data elements; standardisation and interoperability of proprietary data formats; data content standards; and transmission and data tagging standards. The problem is simply that no one organisation has a full-time mandate and the appropriate full-time staff to make this work as a unified effort, which is essential if the referential data that comprise upwards of 70 per cent of the data components of financial transactions are to meet and match seamlessly across the global payment, settlement and clearing mechanisms, or are to be aggregated and reported appropriately to management and boards, and to regulators.²⁷

SYSTEMS AND FACILITIES

The current state of implementation of data management systems within financial enterprises and infrastructure facilities is also evolving. These systems exist both as newly implemented centralised activities supporting multiple business applications and also as separate processing components incorporated within each legacy business application. The present situation of separate business units incorporating duplicate reference data acquisition, separate databases for storing this data, and the subsequent processing duplication across the many silos is slowly giving way to more integrated systems across the enterprise, not only for reference data but for transaction processes aligned with these business units. This is due, in part, to the continuing evolution toward all electronic order management and trade execution systems and the practice of integrated trading of products with instruments for hedging risk or conversion into other currencies. It is also due to the evolving real-time availability of lockedin quotes and last sale price information from all-electronic trading venues and the integration of executed trades within firm-wide financial, risk management and P&L reporting systems.

The importance of reference data systems can be understood by recognising that all financial transactions are represented as data in information systems. If the data are wrong, the transaction does not settle. The retail and manufacturing industries understood this issue a long time ago and standardised around universal barcode identifiers for products and electronic data interchange standards for communicating across suppliers, distributors and retailers. The financial industries’ clearing and settlement infrastructure similarly has such identifiers for financial products; supply chain participants (counterparties, financial intermediaries, corporations, issuers, etc); financial markets and currency designations; valuation and market prices; and other referential information such as credit ratings and economic data used in valuation models.

However, financial industry reference data that should be standardised and identical across each organisation and across the industry are not. These data are sourced independently, with each financial institution performing duplicative functions in an attempt to represent each unique product, business entity and valuation price identically, but failing to do so.²⁸ The consequence is that proprietary and conflicting identification codes exist across the entire range of referential data, including such fundamental identifiers as symbols for corporate issuers, symbols used in contract markets, numbering conventions for securities, supply chain business entity identifiers, and counterparty identifiers.

To compound the problem, clearing and settlement systems operators, other infrastructure facilities and vendors, and even regulators maintain proprietary codes and duplicate sourcing and maintenance functions. Even dates and rates for corporate events and valuation prices for all manner of traded financial instruments are obtained and organised in this manner. Thus, the effect on operating costs and operational risk in faulty data entering clearing and settlement systems is significant. In fact, those infrastructure institutions that operate clearing and settlement systems have capital structures that are, in part, supporting the risk of mismatched transactions caused by faulty data.

All central counterparty markets, central securities depositories (CSDs), payment networks, and novational clearing systems (collectively the global Clearing, Payment and Settlement System) have priced into their services the transaction costs supporting such processing activities as matching of counterparties and the details of the transaction; the reconciliation and workflow processes when transactions do not match; and additional capital for protecting against losses incurred from failures in transactions to be reconciled in a timely fashion. In addition, members assure each other through capital contributions to the central counterparty entity to prevent against default risk. In fact the existence of these facilities are, in the main, simply to assure participants that what was agreed to earlier in the first instance of executing the capital, contract or currency market transaction gets paid for (settled). The bilateral counterparty dealer markets are not centralised and do not fit within the global clearing and settlement system, but they are now being induced to do so by regulatory fiat.

In the OTC derivatives markets, a mutualised risk mitigating facility as proposed by the US President’s Working Group²⁹ and supported by former US Treasury Secretary Paulson’s blueprint for payment and settlement systems³⁰ would be a welcome addition to reducing the risk at the root of intertwined financial institutions. Acting as the central counterparty for an integrated operational infrastructure, it is intended to support the seamless payment and settlement of OTC derivatives contracts through promoting the standardisation of data and the interoperability of infrastructure components.³¹ Another central counterparty facility, this for Securities Lending and sponsored by the Options Clearing Corporation (OCC) is also underway³², intending to mitigate counterparty risk in this segment of the industry. All such central counterparty activities would enhance participants’ ability to manage counterparty risk through accepted netting, collateral and novation techniques, promote the near real-time reconciliation of portfolios and collateral, and accurately value trades.

CURRENT DATA UTILITY PROPOSALS

Today, there is a plethora of in-progress and proposed solutions to organise data across firms and among regulators. The ultimate aim is to observe financial transactions in a common automated context so that risk can be analysed more frequently without the manual labour that such efforts now require. However, while well intentioned, all of the solutions are aimed quite literally at the wrong end of the problem — when trades are transformed into transactions and positions, not when financial transactions are first assembled.

The Central Counterparties’ (CCPs) proposal for central clearing of OTC derivatives has spawned at least six in-process or proposed new entities, under different regulatory regimes and with proprietary data formats. This has lead the Depository Trust and Clearing Corporation and at least two private sector vendors to suggest a role for themselves in retaining the full details of the underlying trading positions in each of these CCP entities in a central repository to support regulatory oversight and transparency in that market.

The CFTC is proposing additional position reporting requirements for its expected oversight of the OTC derivatives markets in the USA. This process, presumably an extension of agricultural commodity contract market oversight, if implemented in the semi-manual manner as in today’s reporting systems, would be a cumbersome and unwieldy process. It would not be up to the needs of real-time processes, high volumes and standards for identifying data for matching counterparty risk or to accumulating positions in other markets not overseen by the CFTC.

The National Institute of Finance, a proposed US federal agency, is proposing to establish a Federal Financial Data Center (FFDC). The FFDC will be a national repository of financial transactions and position data received from US-based financial institutions and their affiliates. An ancillary data attribute utility would assign standards for business entity identification and maintain fundamental data for use by all US regulators. The SEC’s EDGAR corporate filing system is being transformed into the IDEA database, which will require filers to assign standard tags to meaningful data so that computers and systemic risk regulators can access the data directly.

The European Central bank (ECB) is proposing a reference data utility to store SEC EDGAR-like data for companies in the ECB member countries, along with its own fundamental standard data identifier and attribute repository. Issuers would be mandated to deliver corporate event data and reference data to the utility with data vendors helping issuers fulfill their legal requirement to send standardised data to the repository.

The ECB’s T2S project has an objective to settle the money side of securities transactions through a central facility, using transaction standards that they define for all contributing central depositories. Linkup markets, a joint venture between eight European central securities depositories, is implementing a giant electronic mapping facility to transform and then switch proprietary messages and their proprietary codes to allow for interoperability and for interfacing to T2S.

The Committee of European Securities Regulators (CESR) has many initiatives underway including development of its own central reference data repository to rationalise the transaction reporting required within the Markets in Financial Instruments Directive (MiFid). It also maintains transactions standards for communicating transactions among individual country security regulatory authorities and, of late, has expanded its scope to deal with OTC derivative transaction reporting. Finally, it is developing a central repository of credit rating agencies’ historical performance data.

All of these efforts, however, are interjecting more complexity, more overlap, more government involvement and ownership, more standards that are not global standards, and more silo-based, redundant accumulations of identifying data, financial transaction data and position data, when the industry only needs a nudge from its regulators to rally around a single solution, a global data standard and the mechanism to assign, maintain and distribute standard, assured identifying data sets.

THE DEVIL’S IN THE DATA

Non-standard and faulty data has an impact on the timeliness and accuracy of settled transactions, and on the accuracy of business performance and risk reporting processes. For example, there is no global standard identity for issuers, counterparties or obligors, or any hierarchical structures to link them, thus forcing each financial institution to create their own. As an example, IBM’s common stock has over 25 different identifiers used in proprietary trading systems, payment and settlement systems, and in bookkeeping systems across the globe (see Figure 2). Berkshire Hathaway, as a business entity, has at least seven different identifiers in critical information systems such as the Edgar filing system, S&P’s credit rating services, and in Markit’s CDS price reporting system (see Figure 3).

Having given up the physical forms of validation, retrieving a simple description for a security in order to access the proper internal code, such as is the case with General Electric, can produce errors due to variations of descriptions such as Cap, Cap Corp, Capital Cor, Captl Corp, and Co, each with a different symbol and each with its own separate internal code for completing transactions or measuring risk.

Finally, even retrieving the exact same symbol has different meanings (see Figure 4). While the OCC has just been designated to operate the US Symbol Reservation System for equities exchanges to reserve, select and allocate securities symbols, it will not have jurisdiction over US derivative markets nor for international markets³³.

vThe industry is not without its leaders in standards setting. The Association of 60+ National Numbering Agencies (‘ANNA’) in 2002 authorised Standard & Poor’s and Telekurs to develop and manage the ‘ANNA Service Bureau’. The Service Bureau is tasked with improving all aspects of the timely, accurate and standardised identification of financial instruments and operates as a central hub into receive and consolidate International Securities Identification Numbers (ISINs) from ANNA members and partners. ISIN securities identifiers combine a country code with the local national security identifiers (in the US, it is combined with the CUSIP number) for engaging in cross-border transactions. And the industry is not without its controversies. The European Commission (EC) has opened formal proceedings against Standard & Poor’s (S&P) to look into whether it abused its dominant market position by forcing financial firms to pay for the use of US securities numbering codes when accessing data from third party vendors. The Commission believes S&P might have broken rules by forcing banks and investment funds to pay licensing fees for the use of US ISIN codes in their own databases.³⁴

The Deutsche Borse’s Avox subsidiary is supporting a collaboration of 25+ financial institutions that share their independently sourced business entity data for assuring their accuracy and compatibility. Avox validates, corrects, enriches and maintains business entity reference data. This includes data such as corporate hierarchies, registered address information, industry sector codes and company identifiers. Financial institutions collectively address poor client, issuer and counterparty data quality. The participants, which include some of the largest banks and asset managers in the world, both contribute and subscribe to a shared pool of data.

Collectively, these and all the other varied and disparate standards initiatives have the goal of rationalising the multiple security numbering systems (Symbols, CUSIP, Sedol, ISIN, et al) through the establishment of the Unique Instrument Identifier (UII);³⁵ establishing uniform counterparty and supply chain identifiers, known as the International Business Entity Identifiers (for all businesses that are either regulated or on which due diligence is necessary);³⁶ to establish standards for settlement instructions, known as the Standard Settlement Instruction (SSI);^37,38 to confirm a broadened, internationally compatible list of CFI (Classification of Financial Instruments) codes for instrument types, foreign currencies, etc;³⁹ and to rationalise corporate event announcements.⁴⁰

In a recent EDM Council/IBM data quality study high levels of inconsistencies for a number of critical fields just in the product/instrument database were revealed. The study focused on 8,500 fixed income instruments (highly liquid index constituents) matched on 20 reference data fields among five participating firms.

There was significant variance in the core fields that were used for settling trades and risk/portfolio analysis. For example, approximately 1.5 per cent of the reported values for interest rate and 1.1 per cent of maturity dates were inconsistent with other reported data. Other fields such as issue date, call date, and next interest date had discrepancy rates that ranged from a low of 1.6 per cent to a high of 93.1 per cent. The studies’ authors concluded that actual error rates would likely be higher if the study included more illiquid instruments. Participants had expected a near ‘zero defect’ rate across these core fields (see Figure 5).⁴¹

Compounding the problem further is the introduction of a whole new set of proprietary data tags that can be used by document preparers and transaction originators for identifying elemental data to be selected from a digitised message or an electronic document or used as a self-describing content format for financial transactions. Here regulators across the world, including the SEC, the EC and the Federal Reserve have XML (eXtensible Mark-up Language) tagging initiatives underway to code prospectuses, corporate filings, and mutual fund filings with specific tags.

XML is a way of representing data so that its content is discernable within the message or transport layer. Unlike earlier standards that primarily transported data, this standard imbeds the data’s intent, or content, and structure into the message through the use of tags. The reference data requirements for equities and fixedincome securities and other instruments, such as derivatives, futures, commodities, and options, are also being addressed through incorporation into existing standards and through the establishment of standards within the XML protocol.

The American SEC Edgar database, a repository of corporate financial information, has mandated electronic filing of these reports in XBRL format, a variant of XML. The DTCC has initiated its New Issue Information Data Service (NIIDS) requiring XML tagging. Content standards will ultimately be required as, for example, security or business entity codes will be rejected if they do not match up to the reporting venues (the SEC’s or DTCC’s) master data files (see Figure 6).

Already such organisations as the FISD and the EDM Council have initiated projects to standardise a set of business terms for all the data components required for a market-placed financial transaction throughout its life cycle, with the intent to transform these into a set of XML data tags. The IBM Data Governance Council is promoting standard tagging of loss data in corporate filings for improved risk management. However, these and all the other XML initiatives, if left to their own stand-alone implementations will be introducing a whole new generation of proprietary data tags, thus adding to the existing problem of multiple and proprietary product and business entity identification codes and other data attributes.

OPERATIONAL LOSSES
The majority of operational losses are due to transaction processing errors⁴² — the failure of people, systems and the data they act upon to operate seamlessly, from origination of the transaction through to payment and settlement, referred to as straight-through-processing.⁴³ Surveys have found that in 30 per cent to 45 per cent of the cases of failed transactions the problem lies with faulty reference data.^44–47 The value-at-risk of these mismatched transactions is some significant portion of the estimated US$7.5trn in daily US settlement value at risk in 2007 at DTCC alone. Globally, SWIFT estimated in 2002 that the work effort involved in repairing these mismatched transactions cost the industry US$12bn annually.⁴⁵

Similarly, product inventory adjustments within these systems (due to corporate actions such as mergers or tenders) and additional cash flows from dividends and interest payments on this inventory are subject to losses due to data errors. Financial enterprises occasionally receive incorrect adjustment information or payments that they then apply erroneously to their product inventory or pay to beneficial owners. In some instances such events are completely missed or go unreported. These corporate directives are usually unstructured text published as a press release or regulatory filing and interpreted through independently sourced reference data intermediaries (see Figure 7).

The value of losses due to faulty corporate action data is reported by US firms to be 5 per cent to 10 per cent of their operational costs for processing corporate actions. In a study conducted for the DTCC in 2004, industry trading losses of e1.5–e8bn annually was estimated due to faulty corporate action data.⁴⁶ DTCC reported processing nearly US$3.5trn of corporate action value in 2007.⁴⁷ Initiatives in the USA are underway to create ‘at source’ (directly from the reporting corporations) corporate event announcement templates and standards for this data for direct entry to the SEC’s EDGAR corporate filing system.

Basel II, in classifying operational loss events, includes the category of execution/delivery/process management losses.⁴⁸ Many such recent losses can be attributed in part, if not in total, to data problems. Citibank reported, for example, that its market value-at-risk number does not include CDO positions because they are hard to value in the absence of prices or model inputs;⁴⁹ Credit Suisse took a US$2.8bn write-down for valuationmodel pricing errors and use of stale prices;⁵⁰ Socie´te´ Ge´ne´rale reported a US$4.9bn loss from trader fraud where improper counterparty codes were used and no systematic ability existed to look across proprietary systems position data and external exchange position data;⁵¹ and Bear Stearns nearly collapsed because it could not price its mortgage portfolios, among other things.⁵²

SUMMARY

By any standard, the costs and operational risk consequences of faulty data used in non-standardised payment, clearance and settlement systems is significant. Failed transactions and reporting processes need to be either manually reprocessed and/or reported into spreadsheets where they can be controlled, investigated, repaired and then reprocessed. Additional verifications and reconciliations are introduced to control the multiple data sources that have to be created in manual workarounds and spreadsheets outside their respective automated payment and settlement processing systems. The usual solution, cross-mapping of the disparate islands of redundant data as a recent development, Linkup- Markets is attempting to do with eight European central securities depositories, builds new technology costs into the information infrastructure along with new operational costs to accommodate the inevitable reconciliation process that follows from data failures in cross-mapping everchanging proprietary coding structures.⁵³

Through collaboration, mutualised risk mitigation undertakings in this area can benefit the entire global financial industry.⁵⁴ Interconnected enterprises electronically traverse a global communication grid at the core of the world’s economic activity, doing so through financial transactions built from component data elements that are independently sourced and created. In that grid, the goal is to complete transactions seamlessly and in real-time. This requires that each company identify its referential data identically, a lofty goal yet unaccomplished and destined never to be identical due to the inherent risk built into the industries’ current best practices of multiply-sourced and non-standard data inputs. It is these faulty ‘best practices’ that are at the root of much operational risk and lack of transparency throughout the global financial industry.

A new industry-wide best practices paradigm is advised to realise the objectives of reduced systemic risk and significant operational efficiencies. A partnership with emerging global regulators, systemically important financial enterprises, and infrastructure institutions is necessary to make mandatory a single standard for product and business entity codes while allowing for a reasonable implementation period. Precedents from phasing in standard coding structures from earlier efforts accomplished on a countryby- country basis and implemented in the late 1960s–1970s should be informative.

Prodded in the past by massive operational failures, industry members in each sovereign domain began the phase-in of new, standardised numbering conventions by first requiring that the new conventions be used exclusively externally — between interacting financial enterprise, between them and their then emerging centralised clearing and depository facilities, and for regulatory reporting purposes. Internal changes were left to each organisation, and each vendor under their own timetable to phase out their own internal proprietary codes, some of which took decades to accomplish.

The benefit of more positive matching between interconnected institutions was thus accomplished with this basic structure evolving into today’s globally interconnected best practices mechanisms with slight modifications. These modifications, such as: external post trade mapping facilities such as Omgeo and Linkup- Markets; attempts at ‘universal’ coding structures for financial instruments, such as the ISIN number; and still unresolved standards for business entities involved in the global supply chain, have left the industry with increased global systemic risk, a lack of transparency and a high cost processing environment with no chance of moving beyond incremental change.

CONCLUSION

While further initiatives for enterprise data management and outsourcing will continue, these efforts will not solve the problem of excessive costs and systemic risk; the CCDM will. Multi-sourced, multiple copies of non-identical reference data cannot solve these problems permanently, even when all are using the same transmission standards, standard data tags or content standards, or when everyone has one golden copy in their own firm’s or in each central securities’ depository or clearing facility, or in collective facilities that serve multiple firms. Systemic risk and excessive cost would still be built into the industry’s infrastructure due to the still unmitigated risk and duplicated costs from:

• the limited availability of budgets of each firm or facility to source data directly and/or from multiple vendors;

• different vendors chosen for each firm or existing infrastructure facility thus imbedding a variance in the data sets maintained by each firm and each outsourced facility;

• each firm/facility with different rules for accepting ‘best-of-breed’ data;

• duplicated activities and costs for each firm/facility essentially performing the same tasks for an identical outcome;

• regulators and firms still dealing with faulty definitions of aggregated risk for a counterparty whose hierarchies and definitions of business entities are determined separately by each firm/vendor;

• firms still only finding out data faults when they try to send a transaction through its settlement process and it fails to complete;

• the industry still lacking the ability to accommodate STP in any time frame approximating trade date settlement, let alone real-time settlement;

• regulators still rejecting electronically filed regulatory reports because they can’t match incoming data sent electronically from firms to regulators’ databases; and

• regulators accepting electronically filed reports because they did match incoming data from firms, but the regulators’ databases had different meanings (descriptions of business entities, instrument identities, data attributes, etc) for the matched data elements.

A globally centralised governance body, such as the proposed Central Counterparty for Data Management, chartered and supported by global regulatory institutions (ie the Basel Supervisory Committee on Capital Adequacy or the Financial Stability Board) and made up of a group of the world’s leading, systemically important financial enterprises and their infrastructure institutions can solve this problem, indeed actually accelerate their own internal EDM or outsourcing projects, by collaborating on the creation of the CCDM at this critical time.^55–57

Are the industry leaders up to the task? There are only a handful of large, systemically important financial enterprises that interact with the vast majority of smaller securities firms, banks, insurance companies, investment managers and hedge funds in the capital, contract and investment markets. These smaller firms, in turn, use these larger firms’ services as traders, investment managers, prime brokers, paying agents, servicing agents, trustees, fiduciaries, escrow agents, clearing agents and custodians. These systemically important financial enterprises, 19 are so designated in the USA alone, are also the key members of the systemically important financial infrastructure entities that constitute the global payment, clearance and settlement system. They bear much of the burden of allocating capital to support the guarantees and risk management practices of these industry-wide risk mitigating entities. At DTCC and its clearing and settlement subsidiaries alone, they collectively held US$19.7bn of such participants’ funds at year-end 2007.⁵⁶

In the end we should all be able to relate to the benefit of regulatory support for societal benefit. We need only look at the recent US Federal Communication Commission’s policy change on cell phone numbers. Prior to mandating a single, universal and portable number for an individual or business entity, each provider of network services had assigned unique numbers to their own clients. Switching costs, both in monetary terms and measured in terms of the risk of losing calls and contacts, was a high hurdle to overcome. Even though services were poor or deteriorating, people were reluctant to switch because it meant losing their assigned number.

This paper presents a case for industry members to embrace the CCDM towards a similar end. Each product or business entity would be assigned a universal, portable, standard number or code, accessible from a centralised database. To follow the telephone analogy, as 411 provides a universal directory of telephone numbers, think of electronically dialling ‘CCDM’ for the universal set of data components for each financial product or business entity.

REFERENCES

(1) Bank for International Settlements, Committee on Payment and Settlement Systems, The interdependencies of payment and settlement systems, June 2008: ‘In addition, direct relationships among systems may facilitate a reduction in specific sources of operational risk by favouring the standardisation, automation and integration of different payment and settlement processes. Such developments in the functioning of payment and settlement processes can reduce the complexity of payment and settlement operations and minimise the potential for human error. As a result, key sources of operational risk can be eliminated.’

(2) Former US Treasury Secretary Henry M. Paulson Jr. in announcing his Blueprint Report for Market Structure Improvements, March, 2008: ‘Our current regulatory regime is almost solely focused above ground, at the tree level. The real threat to market stability is below ground, at the root level, where the health of financial firms is intertwined.’

(3) US Treasury Secretary Elect Timothy Geithner, June, 2008: ‘Systems underpinning global financial markets are becoming more interconnected in increasingly complex ways.’

(4) Financial Services Authority, Clearance and Settlement, 27th March, 2009, available at: http://www.fsa.gov.uk/ pages/About/What/International/pdf/ CSD.pdf.

(5) CESR/ESCB, Consultation Paper Draft Recommendations for Securities Settlement Systems and Draft Recommendations for Central Counterparties, October 2008, available at: http://www.cesr-eu.org/ index.php?page=response_details&c_ id=124&r_id=4526.

(6) US Department of Treasury, The President’s Working Group on Financial Markets — Statement by the President’s Working Group on Financial Markets, 6th October, 2008, HP-1177.

(7) G-20, Declarations on Strengthening the Financial System — London, 2nd April, 2009.

(8) Financial Stability Board, Re-establishment of the FSF as the Financial Stability Board, Prepared remarks by Mario Draghi, Chairman of the Financial Stability Forum, at the conclusion of the London Summit, 2nd April, 2009, available at: http://www.fsforum.org/publications/ r_090402.pdf.

(9) CPSS-IOSCO (2001) Recommendations for securities settlement systems, Committee of Payment and Settlement Systems and the Technical Committee of the International Organization of the Securities Commissions, available at: http://www.bis.org/publ/cpss46.pdf.

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(11) The Giovannini Group (2001) Cross-Border Clearing and Settlement Arrangements in the European Union, Brussels, available at http://europa.eu.int.

(12) The Giovannini Group (2003) Second Report on EU Clearing and Settlement Arrangements, available at: http://europa.eu.int.

(13) Milne, A. and Tang, L. (2005) ‘An economic analysis of the potential benefits and dis-benefits of faster payments clearing’, Cass Business School, May.

(14) Milne, A., ‘Standards Setting and Competition in Securities Settlement’, Bank of Finland Research Discussion Paper No. 23/2005, available at SSRN: http://ssrn.com/abstract=1019990.

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(16) ‘How to fix financial reporting, improving transparency and accuracy of financial info could help rebuild shattered investor confidence say financial experts. Here’s what they think should be done’, Business Week, 10th November, 2008.

(17) Chisholm, M. (2000) ‘Managing Reference Data in Enterprise Databases: Binding Corporate Data to the Wider World’, The Morgan Kaufmann Series in Data Management Systems, August, p. 3.

(18) Federal Register, Vol. 72, No. 39, Wednesday, 28th February, 2007, Notices.

(19) Milne, A. (2007) ‘The industrial organisation of post-trade clearing and settlement’, Journal of Banking & Finance, Vol. 31, No. 10, pp. 2945–2961.

(20) The European Central Bank, Remarks on the future of European financial regulation and supervision, Keynote address by Jean-Claude Trichet, President of the ECB at the Committee of European Securities Regulators (CESR), Paris, 23rd February, 2009 ‘creating a standard for reference data on securities and issuers, with the aim of making such data available to policy-makers, regulators and the financial industry through an international public infrastructure’.

(21) Grody, A. D., Harmantzis, F. and Kaple, G. J. (2007) ‘Operational risk and reference data: exploring costs, capital requirements and risk mitigation’, February, p. 44, working paper, available at: http://ssrn.com/ abstract=849224 (accessed 2nd August, 2008), originally presented at the Financial Management Association, European Conference, Stockholm Sweden, 9th June, 2006, Session 47, available at: http://www.fma.org/ Stockholm/StockholmProgram.pdf, (accessed 2nd August, 2008).

(22) Group of Thirty, Global Clearing and Settlement Committee — Final Monitoring Report, May 2006: ‘The implementation of reference data standards has proven difficult. With no global owner of reference data and friction between the needs of the domestic and cross-border market users, progress has been slow. Future progress will require greater efforts by market infrastructure operators and international institutions with global reach.’

(23) Grody, A. and Mark, R. (2007) ‘Response to Federal Register’, Vol. 72, No. 39, Wednesday, 28th February. Request for Comment on Supervisory Guidance for Basel II Implementation’, available at: http://www.federal reserve.gov/SECRS/2007/July/20070 717/OP-1277/OP-1277_16_1.pdf (accessed 2nd August, 2008).

(24) Basel Committee, Banking Supervision’s Joint Forum, Cross-sectoral review of group-wide identification and management of risk concentrations, April 2008, available at: http://www.bis.org/publ/joint19.pdf? noframes=1, ‘it is clear that risk concentrations may arise from (interrelated) exposures across the risk categories, rendering a silo-based approach insufficient as potential concentrations across categories may not be captured’ and ‘firms reported significant differences across entire risk systems (eg, risk typology, risk metrics, mathematical and statistical risk measures, historical IT systems etc) as important impediments to the integration process.’

(25) Grody, A., Mark, R. and Hughes P. (2008) ‘Operational risk, data management and economic capital’, Journal of Financial Transformation, Cass Institute on Risk, June, available at: http://www.capco.com/files/pdf/62/ PART%202/Operational%20risk,%20 data%20management,%20and%20 economic%20capital.pdf (accessed 2nd August, 2008).

(26) Grody, A.D. (2008) ‘Payment and settlement systems: The case for mutualised risk mitigation within the Basel II framework’, Journal of Risk Management in Financial Institutions, Volume 1 No. 4.

(27) SEC, 17th April, 2001, ‘Global Joint Venture Matching Services — US, LLC; Order Granting Exemption from Registration as a Clearing Agency’, available at: http://www.sec.gov/ rules/sro/34-44188.htm.

(28) Nelson, M. (2006) ‘TowerGroup: Measuring the securities industry progress on reference data, 2002–2005’, TowerGroup, February.

(29) De Weirdt, M, (2005) ‘Standards — Balancing the vision with the Pragmatic’, Journal of Financial Transformation, SIBOS Special issues paper Transformation 2 — Vol. 16.

(30) Financial Information Forum, Market Data Capacity Charts, March 2008 Data — Excerpt available at: http://www.fif.com.

(31) Ibid. see Ref. 3.

(32) EDM Council, Data Quality — Comparison and Analysis of Securities Reference Data, 6th January, 2009.

(33) President’s Working Group on Financial Markets (2008) ‘Policy statement on financial markets regulation’, available at: http://www.ustreas.gov/ press/releases/reports/pwgpolicystate mktturmoil_03122008.pdf, (accessed 2nd August, 2008).

(34) US Department of the Treasury (2008) ‘Blueprint for a Modernized Financial Regulatory Structure’, available at: http://www.treas.gov/press/releases/ reports/Blueprint.pdf (accessed 2nd August, 2008).

(35) ‘The Clearing Corporation and The Depository Trust and Clearing Corporation announce credit default swap (CDS) clearing facility linked to DTCC’s trade information warehouse’, press release, 29th May, 2008, available at: http://www.clearingcorp.com/ press/pressreleases/20080528- dtcc-cds.html (accessed 2nd August, 2008).

(36) Options Clearing Corporation Press Release, OCC Formalises Agreement with Quadriserv to Launch Centralised Securities Lending Marketplace, 7th January, 2009.

(37) Kentouris, C. (2009) ‘OCC Will Operate US Symbol Reservation System, Securities Industry News, 17th February.

(38) Kentouris, C. ‘EC To Investigate S&P for Securities ID Fee Policies’, Securities Industry News, 12th January, 2009.

(39) Atkin, A. (2005) ‘Reference Data and Unique Instrument Identification’, presented at the Financial Institution’s Management Conference, 8th February.

(40) ISITC-Europe IT Group/RDUG & ISITC RDUG BEI Initiative Requirements Overview, 5th September, 2005.

(41) SSI Fresh White Paper — Defining an industry-endorsed approach to improving SSI enrichment, November 2004.

(42) ISITC, Market Practice Recommendation for the Communication of Standing Settlement Instructions FINAL Version 1.0, ISITC Reference Data Working Group, December 2008.

(43) Reference Data User Group (2003) ‘Industry reference data issues’, 10th February, 2003, available at: http://www.fisd.net/referencedata/ 20030210rdugindustryissues.pdf (accessed 2nd August, 2008).

(44) Reference Data User Group (RDUG) & Corporate Actions Working Group (CAWG) Interim White Paper, December 2004.

(45) Ibid. see Ref. 32.

(46) US Federal Reserve, The Quantitative Impact Study 4 (QIS4) and the Loss Event Collection Exercise, May 2005.

(47) Grody, A. (2006) ‘Solving the Reference Data Problem in Financial Services — Are we on the right path?’, Journal of Operational Risk, Vol. 1, No. 3, pp. 63–69.

(48) Securities Industry Association, Final T+1 Report, August, 2000.

(49) Reuters (2001) Capco, TowerGroup, Reference Data: The Key to Quality STP and T+1, 15th October.

(50) Nelson, M. (2006) The State of Reference Data Management: Results from the 2005 TowerGroup Reference Data Survey, February.

(51) Scher, D. (2008) The State of Reference Data Management: Results from the 2008 TowerGroup Reference Data Survey, September.

(52) SWIFT — Results of STP Reviews Reported on in 2002.

(53) Oxera/Depository Trust and Clearing Corporation (2004) ‘Corporate action processing: what are the risks?’, May, available at: http://www.oxera.com/ main.aspx?id=222 (accessed 2nd August, 2008).

(54) Depository Trust and Clearing Corporation, Annual Report, 2007, available at: http://www.dtcc.com/ downloads/annuals/2007/2007_ report.pdf (accessed 2nd August, 2008).

(55) Ibid. see Ref. 19.

(56) Citigroup (2008) ‘Citi reports fourth quarter net loss of US$9.83bn, loss per share of US$1.99’, press release, 15th January, 2008, available at: http://www.citi.com/citigroup/press/ 2008/080115a.htm (accessed 2nd August, 2008).

(57) Borio, C. (2008) ‘The financial turmoil of 2007–?: A preliminary assessment and some policy considerations’, BIS Working Paper No. 251, Monetary and Economic Department, Bank for International Settlements, Basel Committee.

(58) Socie´te´ Ge´ne´rale (2008) ‘General Inspection Department, Mission Green Summary Report’, 20th May, 2008, available at: http://www.socgen.com/ sg/file/fichierig/documentIG_5197/ rapportmissiongreen.pdf (accessed 2nd August, 2008).

(59) ‘Bear’s Market’, Wall Street Journal, 4th April, 2008, p. A12.

(60) Link Up Markets at http://www.linkupmarkets.com/.

(61) Nelson, M. (2006) ‘TowerGroup: Measuring the securities industry progress on reference data, 2002–2005’, TowerGroup, February.

(62) IBM Global Business Services, Expanding the Innovation Horizon — The Global CEO Study, 2006, ‘Defy collaboration limits — Collaborate on a massive, geography — defying scale to open a world of possibilities’.

(63) DTCC, 2004, Annual Report.