Traditional Approaches to Credit Risk Measurement 20 years of modeling history

2 Expert Systems – The 5 Cs Character – reputation, repayment history Capital – equity contribution, leverage. Capacity – Earnings volatility. Collateral – Seniority, market value & volatility of MV of collateral. Cycle – Economic conditions. – recession default rates >10%, : < 3% p.a. Altman & Saunders (2001) –Non-monotonic relationship between interest rates & excess returns. Stiglitz-Weiss adverse selection & risk shifting.

3 Problems with Expert Systems Consistency –Across borrower. “Good” customers are likely to be treated more leniently. “A rolling loan gathers no loss.” –Across expert loan officer. Loan review committees try to set standards, but still may vary. –Dispersion in accuracy across 43 loan officers evaluating 60 loans: accuracy rate ranged from Libby (1975), Libby, Trotman & Zimmer (1987). Subjectivity –What are the optimal weights to assign to each factor?

4 Artificial Neural Networks Computerized expert systems attempt to replicate the judgment of experts using computerized decision making models. Elmer & Borowski (1988): expert systems correctly predicted 60% of failures 7-18 months before bankruptcy, whereas credit scoring models only forecast between 33% - 48% of failures.

5 Disadvantages of Induction- Based Expert Systems The time and effort required to translate the human experts’ decision processes into a system of rules. The difficulty and costs associated with programming the decision algorithm and maintaining the system. The inflexibility of the expert system to adapt to changing conditions.

6 Artificial Neural Networks Address These Problems Simulates the human learning process. Can make “educated guesses” when data are incomplete or noisy. Can adapt to changing conditions by continuing the “learning” process. Can incorporate non-quantifiable, subjective information into decision.

7 How do Neural Networks Work? Figure 2.1 Inputs –Data inputs: company financial statements Weights –Relative importance assigned to each data input in determining the value of the hidden units. Hidden Units –Output variables that are joined together to produce decisions.

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9 Disadvantages of Neural Networks Can get very large quickly. 10 inputs and 12 hidden units produces 4.46 x possible network configurations. Can be over-trained to a particular database. Lack of transparency. Cannot check decisions since intermediate steps are hidden and may not be duplicated.

10 Tests of Neural Networks To estimate external credit ratings: –Moody & Utans (1995): outperform linear regressions in classifying bond ratings. –Singleton & Surkan (1995): 73% accuracy in predicting bond ratings whereas only 57% accuracy for a credit scoring model. To estimate bankruptcy. -Kim & Scott (1991): for 190 Compustat firms: predicts 87% of bankruptcies in year of bankruptcy, 75%, 59%, 47% 1-year prior, 2-years prior, and 3-years prior

11 Rating Systems Oldest Loan Rating System – OCC 5-point system: four low quality & one hi quality. –Pass/Performing0% loan reserve –Other assets especially mentioned (OAEM) 0% –Substandard Assets 20% –Doubtful Assets 50% –Loss Assets 100%

12 Natl. Assoc. of Insurance Commissioners (NAIC) Ratings NAIC Rating Capital Reserve (life insurance) 1 AAA,AA,A 0.3% 2 BBB 1% 3 BB 4% 4 B 9% 5 < B 20% 6 Default 30%

13 Internal Ratings at Banks 60% of US BHCs have internal ratings on a 1-10 scale covering 96% of large & mid sized corporate loans, 71% of small corp. loans, and 54% of retail loans. Internal ratings based on PD (60% of systems) or PD and LGD (40%). Most often used for risk reports & loan pricing. Other uses shown in Figure 2.2. Will be used for BIS II.

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15 Problems with Internal Ratings Must be validated using large amounts of data & subjective factors. If used for regulatory capital purposes, concerns about integrity of system – incentive to “shade” ratings. Ratings must be transparent – consistent and compatible. Ratings must be flexible and responsive to changing conditions.

16 Credit Scoring Models Linear Probability Model Logit Model Probit Model Discriminant Analysis Model 97% of banks use to approve credit card applications, 70% for small business lending, but only 8% of small banks (<$5 billion in assets) use for small business loans. Mester (1997).

17 Linear Discriminant Analysis – The Altman Z-Score Model Z-score (probability of default) is a function of: –Working capital/total assets ratio (1.2) –Retained earnings/assets (1.4) –EBIT/Assets ratio (3.3) –Market Value of Equity/Book Value of Debt (0.6) –Sales/Total Assets (1.0) –Critical Value: 1.81

18 Problems with Credit Scoring Assumes linearity. Based on historical accounting ratios, not market values (with exception of market to book ratio). –Not responsive to changing market conditions. –56% of the 33 banks that used credit scoring for credit card applications failed to predict loan quality problems. Mester (1998). Lack of grounding in economic theory.