Bayesian Methods for Improving Credit Scoring Models

Peter N. Posch (), Loeffler Gunter and Schoene Christiane
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Loeffler Gunter: University of Ulm
Schoene Christiane: University of Ulm

Finance from EconWPA

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 (search for similar items in EconPapers)
JEL-codes: C11 G21 G33 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-cmp, nep-ecm and nep-fin
Date: 2005-05-31
Note: Type of Document - pdf; pages: 27
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Persistent link: http://EconPapers.repec.org/RePEc:wpa:wuwpfi:0505024

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