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Bayesian Methods for Improving Credit Scoring Models

by Gunter Löffler of the University of Ulm,
Peter N. Posch of the University of Ulm, and
Christiane Schöne of the University of Ulm

May 31, 2005

Abstract: We propose a Bayesian methodology that enables banks to improve their credit scoring models by imposing prior information. As prior information, we use coefficients from credit scoring models estimated on other data sets. Through simulations, we explore the default prediction power of three Bayesian estimators in three different scenarios and find that they perform better than standard maximum likelihood estimates. We recommend that banks consider Bayesian estimation for internal and regulatory default prediction models.

JEL Classification: C11, G21, G33.

Keywords: Credit Scoring, Bayesian Inference, Bankruptcy Prediction.

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