Exam 10: Credit Risk I: Individual Loan Risk

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Discriminant models divide borrowers into high or low default risk classes contingent on their observed characteristics (Xj). Similar to linear probability models, linear discriminant models use past data as inputs into a model to explain repayment experience on old loans. The relative importance of the factors used in explaining past repayment performance then forecasts whether the loan falls into the high or low default class.
In Altman's linear discriminant model, the indicator variable Z is the overall measure of the default risk classification of a borrower.This in turn depends on the values of various financial ratios of the borrower (Xj) and the weighted importance of these ratios based on the past observed experience of defaulting versus non-defaulting borrowers derived from a discriminant analysis model. According to Altman's credit scoring model, a score of less than 1.81 would place the potential borrower into a high default risk category. Any score above 2.99 is regarded as a low default risk, while a score between 1.81 and 2.99 is in the 'zone of ignorance', where a borrower may or may not default.
Several criticisms have been levied against linear discriminant models. First, the models identify only two extreme categories of risk: default or no default. The real world considers several categories of default severity. Second, the relative weights of the variables may change over time. Further, the actual variables to be included in the model may change over time. Third, these models ignore important, hard-to-quantify factors that may play a crucial role in the default or no default decision. For example, the reputation of the borrower and the nature of long-term borrower-lender relationships could be important borrower-specific characteristics, as could macroeconomic factors, such as the phase of the business cycle. Fourth, no centralised database on defaulted business loans for proprietary and other reasons exists. This constrains the ability of many FIs to use traditional credit scoring models (and quantitative models in general) for larger business loans.