August CRISIL Insight. Expected Loss Based Provisioning

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1 August 2012 CRISIL Insight Expected Loss Based Provisioning

2 CRISIL Insight Contacts Sharad Kumar Director, CRISIL Risk Solutions Tel.: Neelesh Pal Associate Director, CRISIL Risk Solutions Tel.: Abhishek Jhawar Manager, CRISIL Risk Solutions Tel.:

3 YEARS M A K I N G M A R K E T S F U N C T I O N B E T T E R P a n In day to day life, we do financial planning taking into account future foreseeable risks. This financial planning is nothing but provisions made to meet expected losses which we provide for before we actually incur a loss. Banks should adopt similar principles while planning their provisions for meeting their loan losses. Various studies have indicated that the incurred-loss based provisioning framework leads to pro-cyclicality effect in the loan loss reserves of a bank. During the good times, banks maintain low loan loss reserves, whereas during tough times, higher reserves are maintained. This has an adverse impact on the profitability and acts as fuel to the already burning economic situation. Through this note, we present the importance of expected loss based provisioning which ensures that the provisions are based on quality of the portfolio rather than ultimate outcome of the same. Expected Loss (EL) Expected Loss (EL) based provisioning framework is a forward-looking technique, which assesses the provisioning requirement for a bank on the basis of the credit quality of its own portfolio. A key benefit of adopting this framework, over traditional approaches, is that it makes provisioning requirements dynamic & portfolio sensitive and helps banks to build buffer during boom times, which can be utilized during stress periods. Why Post the financial crisis of 2008, regulators globally are reviewing the banking guidelines to ensure viability of banking institution under severe economic scenarios. One of the many topics being discussed intensely is the management of capital funds of a bank. The provisioning framework has direct impact on the profitability/ reserves of a bank and thereby its capital funds. Even accounting ombudsman viz. IASB and IFRS Foundation have clearly stated their intention to migrate the current accounting practices for recognition of financial assets (IAS 39) from an incurred-loss based approach to a more dynamic expected-loss based approach. Though deliberations on the final approach are on, one of the most widely discussed approach under the expected-loss mechanism is the PD-LGD method.

4 CRISIL Insight How An expected-loss based provisioning framework requires banks internal estimates of the key loss parameters, viz. Probability of Default (PD), Loss given Default (LGD) and Exposure at Default (EAD). Although the estimation of EL based provisioning (using the risk parameters) is a relatively straight-forward process, estimating these risk parameters requires extensive historical data and comprehensive framework. While the specific provisions would be based on Discounted Cash Flow (DCF) method under the expected loss based method, the key challenge would be to estimate general provisions, which is a function of risk parameters. EL Based Provisioning Framework Classification Bank Portfolio Estimation Approach EL Based Provisions Performing Loans Non Retail Retail Borrower/ Exposure level PD, LGD, EAD Pooled PD, LGD, EAD General Provision Non Performing Loans Non Retail & Retail Discounted Cash - flow Method Specific Provision The estimation of key risk parameters requires banks to Assess the default rates for historical portfolio/ segments and convert these default rates into forward looking through-the-cycle estimate of Probability of Default taking into account macro-economic indicators using regression and advanced statistical techniques. Estimate the Loss given default (LGD) based on losses incurred on defaulted loans. A typical LGD modeling framework requires historical information about the loans, recoveries, collaterals etc. for defaulted exposures. Calculate the exposure-at-default (EAD), which requires the bank to model behavioral patterns like limit utilization for defaulted loans.

5 MAKING MARKETS FUNCTION BETTER YEARS EL based provisioning framework, the risk parameters & approaches to estimate these risk parameters have been presented below. Components & Approaches 1 Probability of Default Weighted PD Transition Matrix based PD 2 Loss given Default Market LGD Implied Market LGD Workout LGD 3 Exposure at Default CCF Estimation for NFB Facilities Historical Limit Utilisation PD L G D EXPECTED LOSS EAD When Right now! With Central Banks chalking out time-lines for banks to migrate to Advanced approaches for credit risk, and accounting regulations under revision, there will never be better time than now to prepare one-self to comply with regulatory requirements and adopt evolved risk management practices. Challenges Select the Expected loss and underlying parameter estimation approach suited to bank s requirements Identify relevant data elements critical for estimating PD, LGD & EAD such as default history, recovery information, etc Conduct Data gathering exercise & assumptions thereof for missing critical information Implement model development and validations frameworks Find risk managers with relevant skill-set How CRISIL Risk Solutions (CRS) Can Help You CRS with its expertise of providing risk management solutions covering consulting services as well as software applications to financial institutions can help you to Understand EL based provisioning requirements Design the risk frameworks customized to suit your bank s products and environment Gather data required for comprehensive assessment of risk parameters Provide independent review of bank s internal estimates of the risk parameters

6 CRISIL Insight About CRISIL Limited CRISIL is a global analytical company providing ratings, research, and risk and policy advisory services. We are India's leading ratings agency. We are also the foremost provider of high-end research to the world's largest banks and leading corporations. About CRISIL Risk Solutions CRISIL Risk Solutions (CRS) provides a comprehensive range of risk management tools, analytics and solutions to financial institutions, banks, and corporates. We have been ranked India s No. 1 Risk Solutions provider in a survey by Indian Banks' Association. We also work in several countries outside India. Our solutions help clients identify, measure, and calibrate a comprehensive range of risks: credit risk, price and market risk, exchange and liquidity risk, operational risk, strategic and regulatory risk. Our expertise in executing and managing diverse risk-related engagements globally, in-depth understanding of geography-specific regulatory and implementation requirements, and a pool of experienced and outstanding professionals enable us to deliver enterprise risk management frameworks from ideation to implementation. We supplement our core consulting strength and analytical skills with robust proprietary software to provide efficient solutions for risk management. Our software and service offerings include internal rating systems to assess the credit worthiness of borrowers, loan origination systems to automate the lending and administration process, automated capital computation processes for credit risk, market risk, and operational risk as per Basel II and economic capital modelling systems. We have adopted global quality assurance standards and are ISO 9001:2008 certified. We are a division of CRISIL Risk & Infrastructure Solutions Limited (CRIS), a wholly-owned subsidiary of CRISIL Ltd. CRISIL Privacy Notice CRISIL respects your privacy. We use your contact information, such as your name, address, and id, to fulfill your request and service your account and to provide you with additional information from CRISIL and other parts of The McGraw-Hill Companies, Inc. you may find of interest. For further information, or to let us know your preferences with respect to receiving marketing materials, please visit You can view McGraw-Hill's Customer Privacy Policy at Last updated: April 30, 2012

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