CRISIL InsightAugust 2012

Expected Loss Based Provisioning

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Contacts

Sharad Kumar

Director, CRISIL Risk Solutions

Tel.: +912233428232

Neelesh Pal

Associate Director, CRISIL Risk Solutions

Tel.: +912233428263

Abhishek Jhawar

Manager, CRISIL Risk Solutions

Tel.: +912233428268

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Expected Loss (EL)

Why

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.

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.

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.

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HowAn 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.

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.

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EL Based Provisioning Framework

Classification

PerformingLoans

NonPerforming

Loans

EL BasedProvisions

GeneralProvision

SpecificProvision

Bank Portfolio

Non Retail

Retail

Non Retail&

Retail

EstimationApproach

Borrower/Exposure levelPD, LGD, EAD

PooledPD, LGD, EAD

DiscountedCash - flow

Method

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When

Challenges

How CRISIL Risk Solutions (CRS) Can Help You

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.

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

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

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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

GL

DPD

EAD

EXPECTED LOSS

EL based provisioning framework, the risk parameters & approaches to estimate these risk parameters have been presented below.

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Last updated: April 30, 2012

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