Bayesian Methods for Improving Credit Scoring Models
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.
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- Grunert, Jens & Norden, Lars & Weber, Martin, 2002.
"The Role of Non-financial Factors in Internal Credit Ratings,"
CEPR Discussion Papers
3415, C.E.P.R. Discussion Papers.
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- Shumway, Tyler, 2001. "Forecasting Bankruptcy More Accurately: A Simple Hazard Model," The Journal of Business, University of Chicago Press, vol. 74(1), pages 101-124, January.
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