An econometric analysis of the bank credit scoring problem

William J. Boyes, Dennis Hoffman, Stuart A. Low

Research output: Contribution to journalArticle

153 Scopus citations

Abstract

Most credit assessment models used in practice are based on simple credit scoring functions estimated by discriminant analysis. These functions are designed to distinguish whether or not applicants belong to the population of 'would be' defaulters. We suggest that the traditional view that emphasizes default probability is too narrow. Our model of credit assessment focuses on expected earnings. We demonstrate how maximum likelihood estimates of default probabilities can be obtained from a bivariate 'censored probit' framework using a 'choice-based' sample originally intended for discriminant analysis. The paper concludes with recommendations for combining these default probability estimates with other parameters of the loan earnings process to obtain a more meaningful model of credit assessment.

Original languageEnglish (US)
Pages (from-to)3-14
Number of pages12
JournalJournal of Econometrics
Volume40
Issue number1
DOIs
StatePublished - Jan 1989

ASJC Scopus subject areas

  • Economics and Econometrics

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