A Nonparametric Approach to Credit Screening

Abstract

Most commonly used credit screening methods are based on Fisher's linear discriminant function for quantitative variates. The variables actually used in credit scoring are usually, however, qualitative occurring as high/low, or present/ absent. We present a nonparametric approach to the credit screening problem. In our method we do not assume multivariate normality nor do we apply any arbitrary scaling procedures to the qualitative variables. We classify an observation to that group with which it has most in common, this being done so as to minimize expected loss from misclassification. It is a variant of the “closest neighbor” rule. The misclassification probabilities of our screening rule are obtained by a jack-knife method. An empirical method for the selection of variates for screening is also given. The possibilities for adopting the method to an on-line computer system is discussed with an illustrative example.

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