Bayesian Methods for Improving Credit Scoring Models
26 Pages Posted: 13 Jun 2005 Last revised: 23 Oct 2010
Date Written: 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.
Keywords: Credit scoring, Bayesian inference, bankruptcy prediction
JEL Classification: C11, G21, G33
Suggested Citation: Suggested Citation
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