Abstract

Mathematical, Statistical Methods for Formalizing the Credit Decision Process

The starting point for the use of mathematical, statstical methods in reaching credit decisions (credit scoring systems) is the wish - to eliminate the weak points in traditional credit appraisal based on subjective yardsticks of value - and to improve profitability by formalized assessments of creditworthiness by making fast but nevertheless sure credit decisions possible. Credit scoring systems work almost exclusively with discriminatory analysis, a method which makes it possible to separate credits into “good” and “bad” on the basis of clearly identifiable attributes of the borrowers. In addition to a description of discriminatory analysis, this contribution deals with the important question of what borrower attributes should be chosen for a credit scoring system. The quality of credit decisions reached with the help of a credit scoring system depends on how far the credit cases on which the method is based satisfy the conditions precedent for the application of statistical probabilities. The so-called forecasting problems become less significant only when a sufficiently large number of approximately similar credit cases are available. This explains the practical importance of credit scoring systems for consumer credits. After implementation of a credit scoring system, the credit rating factors included in the system for every borrower become quantifiable and can be combined additively to give an index number representing creditworthiness. The credit decision is reached by comparing that index number with a predetermined optimal-profit elimination criterion. The so-called critical case is thus sorted out for individual consideration.