Peter O. Davis Partner, Ernst & Young LLP Director of Credit Risk Services peter.davis@ey.com

1. Peter O. Davis Partner, Ernst & Young LLP Director of Credit Risk Services peter.davis@ey.com Current State of Credit Risk Measurement Symposium on Enterprise Wide Risk Management Chicago, April 26, 2004

2. Agenda • Continued Movement Toward Credit Quantification • Regulatory Push • Avoiding Unintended Consequences • Incidence vs. Dollar Based Default Rates

3. Continued Movement Towards Credit Quantification • Extending credit inherently a judgment-based decision • Continued movement toward the reliance on credit models to support credit extension and portfolio management • Driven in part by continued advances in credit risk modeling • More mature models • Greater computing power • Development of credit loss databases • More credit products providing market information • Driven in part by demand for greater transparency • Large defaults by fallen angels triggered increased focus by investors • Demand for greater information by senior management, Board, shareholders, rating agencies, regulators • Demand for consistent measurement across products

4. Regulatory Push • For commercial banks, and (more recently) investment banks, regulators have created incentives for institutions to enhance their internal credit models • For those meeting advanced standards, by year-end 2006, under “Basel II” regulators will rely upon institutions’ internal credit models for setting regulatory capital • Probability of default models • Loss given default models • Exposure at default models • Will result in: • Standardization of credit risk measurement terminology and model classification • Heavy focus on model accuracy • Development of extensive credit performance databases, leading to ongoing innovations in model development • Greater transparency in credit risk-taking across institutions • Greater liquidity and continued innovation in credit products

5. Avoiding Unintended Consequences • As credit models are used more broadly across institutions and more deeply within institutions, continued need to challenge whether: • models accuracy capture risks • model limitations are understood • application of individuals models and the integration of multiple models produce results that are consistent with the intended measurement purpose • Example of the application of default models

6. Incidence-based vs. Dollar-based Defaults Illustration • Obligor default models measure the probability that an individual borrower will default over a given time horizon – an incidence measure of default risk • When measuring expected loss (EL), it is common to use the product of the probability of default (PD), loss given default (LGD) and exposure at default (EAD) • This approach implicitly assumes that incidence-based and dollar-based PDs are the same

7. Illustration Cont’d • Illustration of the impact of dollar-based vs. incidence-based default rates: • Assumptions: • Three banks with loan portfolio of $100 million • Same 10 borrowers and 3 defaults • Obligors have same risk rating and incidence based default rates • LGD = 100% for defaulted loans • 100% closed-end loans

8. Illustration Cont’d • Bank A – Loan Size Does Not Differ • All the loans are the same size • The dollar loss implied by the incidence-based default rate is the same as historical loss • Bank B – Large Positions in Loans to Defaulting Obligors • The dollar loss implied by the incidence-based default rate is based on the number of defaults and average value of the loans • The size discrepancy of the loans are so large the average value hides more than it reveals • Bank C –Small Positions in Loans to Defaulting Obligors • The dollar loss is far lower than the incidence-based default rates imply

9. Loss Severity Adjustment • Loss Severity Adjustment is defined as the ratio of the average value of defaults to the average current balance of the portfolio • For Bank B and C the use of incidence-based PD may not reflect the trends of the portfolio • For such instances a Loss Severity Adjustment may be applied to the losses implied by the incidence-based default rates • After the adjustment, the dollar losses reflect historical figures and the trend in the portfolio towards higher or lower dollar-based default probabilities • Loss Severity Adjustment restores information lost in the averages by reconciling the incidence-based default rates with the banks loss in dollars