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Mahesh Muthu
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Focus on Collateral: Optimizing ISDA CSA Document Metadata

To accurately mine crucial information firms must develop a comprehensive data model to represent CSAs within their systems.

Mahesh Muthu, eClerx
Mahesh Muthu, eClerx

Bilateral OTC Collateral Management

According to the International Swaps and Derivatives Association (ISDA) Margin 2013 Survey, the estimated amount of collateral in circulation is USD 3.7 trillion, as of the end of 2012. The concept of using collateral to mitigate credit risk in the derivatives markets has been in place for decades and is well understood, but what governs the mechanics of such a large pool of assets?

It is important to understand the current state of over-the-counter (OTC) collateral management in order to be well positioned for the flurry of regulatory reforms impacting capital and collateral under the Dodd–Frank Wall Street Reform and Consumer Protection (Dodd-Frank) Act, European Market Infrastructure Regulation (EMIR), Basel III, as well as the Basel Committee for Banking Supervision and International Organization of Securities Commission’s recent joint release, “Margin requirements for non-centrally cleared derivatives.”

The ISDA CSA and Its Business Impact

In the OTC derivatives market, collateral management terms and conditions are largely driven by the framework within the ISDA Credit Support Annex (CSA) document. ISDA CSAs can be heavily negotiated documents, but at a minimum contain terms covering eligible collateral, how collateral can be held and used, as well as terms covering margin calculation and posting. Although the information embedded within these documents seemed of little importance prior to the 2008 financial crisis, many institutions are now investing heavily in people, processes, and technology to mine and actively manage such document metadata.

Throughout the last several years, financial institutions have come to realize the information should be used not only to govern the operational components of collateral management, but that it also plays an important role in trade pricing, collateral optimization and liquidity management.

For example, with respect to trade pricing, cash-collateralized trades should be priced based on the overnight indexed swap (OIS) rate, or most inexpensive to deliver collateral within the eligible collateral schedule in the CSA. A larger list of eligible collateral and currencies in the CSA represents a valuable “switch option” to whichever party is posting collateral. Clients with asymmetrical CSAs, in which only one party must post collateral, or CSAs limited to certain legal entities or products, may turn out to be too expensive to service because of newly introduced capital charges under Basel III. Many firms have established a credit value adjustment (CVA) trading desks to ensure counterparty credit risk is appropriately measured, hedged and priced into across the board trading activity, which requires CSA static data as a key input.

Additionally, the data from the CSA greatly facilitates collateral optimization. Using the static data relating to eligible collateral, haircuts, rehypothecation, etc., along with margin call and asset inventory information, firms can optimize their collateral and reduce funding costs. However, in order for institutions to make informed decisions across these functional areas, they require a well-planned controlled operation to mine information from CSAs.

Data Management Challenges

While most firms have acknowledged the importance of the information able to be garnered from the CSA, many are facing challenges with respect to the accuracy and comprehensiveness of this data stored within internal systems.

A CSA can be decomposed into more than 200 data points. However, many firms have only been capturing approximately 70 data points, on average, from each document. This impacts the ability to perform deeper, more sophisticated analytics and scenario analysis. From those 70 data points, an alarming 65% of agreements have at least one attribute from the CSA coded incorrectly, and 5% of CSAs are unenforceable due to missing pages, agreements, and/or amendments, pointing to a lack of a controlled operation. Compounding this issue, new terms and conditions are appearing in CSAs, further emphasizing the need to have a granular, structured data model and a strong process to ensure there is not basis risk between what is legally agreed and represented within systems.

CSA Data Management Best Practices

It is important that firms develop a comprehensive data model to represent CSAs within their systems in order to accurately mine this crucial information. Ideally, every consuming group, including client on-boarding, legal, the CVA desk, credit risk and liquidity management, should all have input into the list of attributes and data model required for their firm. Additionally, constructing a strong process to ensure the quality of this powerful information is equally important for the organization. For example, pre-validated data capture forms to reduce human error, a data dictionary and data capture rule-set defining how each attribute should be populated, workflow tools for each CSA record to approved and routed to the appropriate consumer, and a metrics and reporting suite to highlight legacy coding errors and outlier agreements which may need to be renegotiated, all are vital parts of this integrated workflow.

Given the extent collateral management is driving business decisions in our current environment both pre and post trade and the pace of regulation, it is very important that the static data in CSAs is represented accurately within financial institutions’ systems to enable both day-to-day operations and strategic decision analytics.

Mahesh Muthu is an Associate Principal – Client Engagement, Financial Services at eClerx. He leads strategic product design based on regulatory mandates and industry trends, as well as IT-enabled outsourcing solutions. Mahesh joined eClerx in 2004 as a Relationship Manager to assist with the growth and management of core accounts and led eClerx’s global expansion across products, departments and functions. Prior to joining eClerx, he worked for Dell Inc where he managed operational expenditures, including forecasting, accounting, reporting and analysis. Mahesh holds a Bachelor of Economics with a concentration in Finance from the Wharton School at University of Pennsylvania.

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IvySchmerken
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IvySchmerken,
User Rank: Author
3/24/2014 | 2:00:53 PM
re: Focus on Collateral: Optimizing ISDA CSA Document Metadata
As banks shift to trading standardized swap contracts via SEFs that are centrally cleared through CCPs, will there be less reliance on the ISDA Credit Support Annex (CSA) documents. It would seem that data capture would be electronic and less prone to error.
Greg MacSweeney
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Greg MacSweeney,
User Rank: Apprentice
3/24/2014 | 1:14:43 PM
re: Focus on Collateral: Optimizing ISDA CSA Document Metadata
It's interesting that the error rates remain so high (65%). After so many initiatives and years of trying to reduce errors, you would think the % would be lower. But, the high % shows you how complex this process really is.
muthum
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muthum,
User Rank: Apprentice
3/20/2014 | 8:58:19 PM
re: Focus on Collateral: Optimizing ISDA CSA Document Metadata
Mike, the primary reasons for high error rates include incorrect data capture, limited (or no) revisions after amendments are made in the system and direct updates to the system without coinciding updates to the agreements. The complexity of these contracts only exacerbates the issue.

An integral component of data extraction is data analytics G㢠benchmarking data with industry practice, comparing data with peer groups, highlighting deviations, addressing risks and current standings. Such insightful information assists banks with better decision making and further helps to prevent high error rates.
muthum
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muthum,
User Rank: Apprentice
3/20/2014 | 8:56:53 PM
re: Focus on Collateral: Optimizing ISDA CSA Document Metadata
ThatGăÍs a great question, Ivy. Banks capture the basic minimum fields required for the collateral management process. However, engaging with a third-party expert, financial institutions can leverage the toolset and processes already developed and mastered, allowing their internal teams such as Credit Valuation Adjustment (CVA) trading desks, Treasury, Finance and Change and Legal to focus on optimizing day-to-day decisions with a more accurate, granular dataset. Banks are making substantial investments in technology to enhance their systems to capture new fields and digitization initiatives directed at specific clauses. Additionally, banks are centralizing documentation data stores technology upgrades to better align data between documentation and reference data departments. We work with banks on multiple such projects to provide electronic records of agreements which are mapped and reconciled to client systems or used as a Gă goldenGăÍ source repository and partner with the multiple departments involved to understand the attributes required.
Mike Lines
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Mike Lines,
User Rank: Apprentice
1/20/2014 | 11:17:01 AM
re: Focus on Collateral: Optimizing ISDA CSA Document Metadata
The high error rates that the banks find in their CSA and Master agreement data stores are a function of the complexity of these contracts and the manual approach they take to data collection. In 2013 Recommind launched the Perceptiv service to automate the extraction of data from ISDA contacts to substantially improve both the speed of data collection and the accuracy of the resulting data.
IvySchmerken
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IvySchmerken,
User Rank: Author
12/24/2013 | 11:09:37 PM
re: Focus on Collateral: Optimizing ISDA CSA Document Metadata
Capturing metadata from the ISDA Credit Support Annex (CSA) documents must be very labor intensive. You say each CSA has about 200 data points and most firms are capturing 70 data points. Plus 65% of the agreements have errors due to incorrect coding while 5% have missing pages. How are firms approaching the analysis of these agreements so as not to miss any vital data?
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