View Point Collateral management changing business / IT landscape - Gurpinder Singh, Anuj Puri, Arshmeet Kaur Abstract Collateral management has been undergoing a major transformation owing to unprecedented change in recent times. The recent financial crisis, along with the freezing of the credit markets and increased volatility has resulted in higher volumes of the collateralized loans, introduction of new security classes as collateral and has also prompted the need for more robust and flexible risk management and valuation systems. The subsequent introduction of regulations in financial markets (BASEL III, CRD IV and Dodd Frank Act) has resulted in increased collateral, as well as the need for incorporating compliance and reporting requirements. This article discusses the business, process and operational impact of the changing business landscape and the resulting transformation in the collateral management ecosystem. www.infosys.com
INTRODUCTION The objectives of market participants as well as the regulators have increasingly become more aligned in reducing the counterparty risk and improving liquidity profile. This is being achieved by reducing the exposure to unsecured credit lending, resulting in a surge of volume of assets under collateral management and addition of different asset types. This has prompted unprecedented changes in the underlying IT and operational systems. This article covers the emerging trends in the business landscape and the subsequent business and IT impact on the collateral management systems. A detailed component view of the collateral management ecosystem in view of the changing business landscape has been outlined. Emerging trends Increased perception of counterparty risk The credit crisis of 2008-2009 highlighted the need to manage credit risk more actively. With more and more banks shutting shop and large financial institutions filing for bankruptcy, the need to stay on top of counterparty credit exposure gained importance. The counterparties are now being carefully and continually assessed for their creditworthiness. Higher haircuts are being proposed for counterparties ranking low on credit worthiness. The lenders are moving towards securitized loans leading to an increase in the use of collateral as a risk mitigation tool. Collateral management has seen a cumulative annual growth rate (CAGR) of around 30% over the last few years (collateralized assets have grown 20X from 2000 to 2009, Source: ISDA Margin Survey 2011). Higher collateral volumes have pushed the financial institutions to opt for robust collateral management and valuation systems (which can handle high collateral volumes and are able to automatically deliver and settle the collateral). There is also a need for systems to be flexible and scalable due to the ever increasing collateral volume. In addition to the traditionally accepted money market and fixed income securities, the financial institutions are accepting diverse and newer security classes like equities, structured products and exotics. The collateral management system thus needs a security reference master to maintain the list of all acceptable securities and a pricing engine to value and provide haircuts. With the expected volatility in the underlying instruments, the haircut computation engine would need to be flexible to compute haircuts on a frequent basis. The pricing and haircut computation engine should get feeds from the market data provider and interact with the risk management system. Price volatility and complex thinly traded instruments Asset valuation data (Market Data) flows in from multiple vendors like Reuters, Bloomberg, MTS, MISTY etc. This implies that the price for a given security could be different across sources. Additionally, for thinly traded securities, requisite data may not always be available and price computation might be required. Complex pricing engines (systems) need to be designed to incorporate various methods to compute the price. Rules engine will need to decide which price to use (price received from market data or the computed price). Market volatility and the introduction of complex and exotic security classes in the collateral management system necessitate the need to establish a robust risk assessment system. Such a system can classify the security classes based on factors like security type, volatility, high/low volumes and compute the haircuts based on security and its classification. An administrative module to detect and override any abnormal activities in the workflow processes like valuation, pricing, haircut calculation along with disbursement of loans will be required. Such systems should be able to publish and distribute frequent reports (like credit limits, asset valuation and abnormal activity) to both the internal stakeholders and the participating clients. High price volatility also calls for a change in the way collateral management for margin calls works. The collateral management system needs to have a robust valuation engine which does the asset valuation at a frequent (pre-defined) time interval and prompts the user to call in more asset (margin call), every time the asset valuation falls. This will ensure that the financial institution is hedged against fall / rise in the asset valuation due to high volatility. Increased risk sensitivity and introduction of sophisticated risk measures The financial institutions are becoming increasingly sensitive to credit risk and are incorporating various risk management measures in their processes. Regulatory authorities like FINRA (US) are laying down various compliance measures, which need to be abided by the financial institutions. The financial institutions are required to maintain a certain level of exposure with their clients and should be appropriately hedged in the event of a default. Also they need to ensure that trading done on behalf of the clients, should meet the compliance requirements. This calls for sophisticated risk management system to be set up as a part of the collateral management. The system should be adept at calculating parameters like Probability of Default (PD), Loss Given Default (LGD) and Exposure At Default (EAD). These will help the lender in making a well-informed decision before lending the money. 2 Infosys View Point
Acceptance of new security classes There is an increasing acceptance of various security classes as collateral by various financial institutions. Earlier, fixed income securities formed a major share of the collateral. Now, new asset classes (equities, structured products and exotics) are increasingly being accepted as collateral. This gives an opportunity both to the financial institutions and the clients to look for more collateral and higher loans respectively using the gamut of securities owned by the client. However, this brings with it the complexity of pricing of securities and the subsequent valuation of assets. Security reference master which has the required information about such securities needs to maintain a list of all acceptable securities. Migrating to enterprise view Financial institutions (lenders) need to limit their risk on a particular security type and diversify their portfolio in terms of exposure to a particular security, security type and the client. This has prompted the need for higher haircuts for a security where the financial institution has a higher exposure. Also the financial institutions need to ensure that they are adequately hedged against concentrated securities across multiple holdings of the same client. This will help in a non-conservative valuation of risk and help them streamline operationally expensive margin calls. As mentioned in the point above there is a need to maintain a security reference master for appropriate valuation of assets and subsequent computation of haircuts. Also, a centralized collateral management system with continual risk assessment is the need of the hour to regulate risk both at the client and the enterprise level. Higher liquidity requirements Basel III norms, CRD-IV and the Dodd Frank Regulation of the US, require the financial institutions to maintain a higher level of liquid assets. Cash, forms an integral part of the collateral management ecosystem and requires the availability of foreign exchange (FX) rates. Fig 1: Operation/IT Impacts of emerging market trends Infosys View Point 3
Collateral management ecosystem Keeping in mind the emerging trends and subsequent business and operational/it impacts; we are proposing a detailed component view of the collateral management ecosystem: Market data provider (1) This will contain security prices, interest rates and credit ratings of the securities. The feed will be used by the proprietary pricing engine for computation of the theoretical price of the security and the haircut computation engine for computation of haircuts. Proprietary pricing engine (2) This will take feed from market data provider and compute the theoretical market price. E.g. inputs for Fixed Income price computation will be coupon, maturity, interest rate, time to next coupon etc. For options, underlying price, beta, theta, interest rates, time to maturity etc. will be used. Rules engine (3) The pricing data will be fed into the rules engine, which would determine the system price, based on pre-defined rules. E.g. take the most conservative price, take the average price, take the market price if the deviation between market and computed price is less than 3% etc. Security reference master (6) This will have the list of all the securities which could be used for collateral and the parameters for computation of prices, as well as haircut. It should be possible for users to edit / add the list of securities and list securities not eligible for lending. Risk management system (7) This will not only compute the exposure of the borrower to a particular asset class, but will compute the exposure of the financial institution to a particular asset class, country risk and do a VAR analysis. Computations can be used for discouraging concentrated positions by applying higher haircut, or out-rightly avoiding certain assets (CDOs/ MBS). This system will also have a capability to generate/distribute reports both at clients and enterprise level. Some of the reports could be asset valuation report, pricing report, abnormal activity report etc. Collateral information master (8) This module will compute the collateral value based on the holding, price and the haircut margin (after applying both asset level as well as currency haircut). The CIM system will update the collateral value in the RTGS system (to be used to compute credit limit for the customer). The CIM system will also provide feeds to the risk management system for computation of exposure etc. Haircut computation engine (4) This will compute the haircut to be applied for each security. This will be computed based on market data (credit ratings, volatility information beta/theta, interest rate fluctuation, currency risk/ fluctuation). Currency haircut value can also computed based on the country credit rating, currency fluctuation, interest rate risk etc. Customer reference master (5) This module will have information on the customer/ financial market participant / bank, and will obtain information on credit line based on the collateral value from RTGS/ collateral information master. The customer information will be updated infrequently (except for the ratings), whereas the credit line/ margins applicable may be computed frequently. 4 Infosys View Point
Fig 2: Collateral management ecosystem component view The proposed collateral management system will ensure that the underlying assets are accurately priced and appropriate business rules are applied to arrive at the valuation and haircuts. Also, the system will have a mechanism to classify high risk securities and restrict their use as collateral. Thus, the proposed system will help the financial institutions make informed lending decisions. Infosys View Point 5
Conclusion The changing business environment and landscape has necessitated a relook at the existing collateral management processes of financial institutions. This is resulting in operational and IT implications, like automated processing, integrated workflow, and investments in risk management and pricing systems etc. The managers must evaluate their existing systems and identify the shortfalls. They can look at deploying the proposed system component wise or as a whole. E.g. an existing system with an EOD price feed can make use of the proprietary pricing engine which can be plugged onto their existing system. It is pertinent for the business, IT and other support organizations to be aware of the underlying business drivers, enabling them to take strategic and operational decisions, and manage the change effectively. 6 Infosys View Point
About the Authors Gurpinder Singh Gurpinder has over 14 years of experience in the financial services industry, in the asset and wealth management domain, in the US, Europe, Middle East and India. He has worked in the areas of portfolio accounting, reporting, performance and performance attribution. In addition to business and process consulting, Gurpinder has extensively worked on requirements gathering and program management for large transformation programs and product development. Gurpinder has a Post Graduate Diploma in Systems and Finance from Indian Institute of Management (IIM), Lucknow, and a B. Tech in Chemical Engineering from National Institute of Technology (NIT), Jalandhar. He can be reached at Gurpinder_singh03@infosys.com Anuj Puri Anuj Puri has 7 years of global experience in financial services industry particularly in Capital Markets and Banking. His areas of specialty include domain and technology consulting and product building for retail and corporate banking. Anuj earned a Master of Business Administration (MBA) degree in International Business from University Business School, Chandigarh. He has lived and worked in India, North America and Europe. He can be reached at anuj_puri@infosys.com. Arshmeet Kaur Arshmeet has 5 years of experience in Business research and consulting services with around 1.5 years of experience in financial services industry particularly in Consumer Banking, Cards and Payment and Capital Markets. She is competent in conducting market intelligence on industry trends, competition, technology solutions and writing research articles, whitepapers, briefing packs/reports. She has worked with senior executives/clients in helping them develop research strategies and creating actionable insights to solve complex client problems. Arshmeet has earned a Master degree in Applied Operations Research (AOR) from Delhi University. She can be reached at Arshmeet_kaur@infosys.com. Infosys View Point 7
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