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Bayesian Methods for Improving Credit Scoring Models

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Author Info

  • Posch Peter N.

    (University of Ulm)

  • Loeffler Gunter

    (University of Ulm)

  • Schoene Christiane

    (University of Ulm)

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.

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File URL: http://128.118.178.162/eps/fin/papers/0505/0505024.pdf
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Bibliographic Info

Paper provided by EconWPA in its series Finance with number 0505024.

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Length: 27 pages
Date of creation: 31 May 2005
Date of revision:
Handle: RePEc:wpa:wuwpfi:0505024

Note: Type of Document - pdf; pages: 27
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Web page: http://128.118.178.162

Related research

Keywords: Credit Scoring; Bayesian Inference; Bankruptcy Prediction;

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  1. Zellner, Arnold & Rossi, Peter E., 1984. "Bayesian analysis of dichotomous quantal response models," Journal of Econometrics, Elsevier, vol. 25(3), pages 365-393, July.
  2. Shumway, Tyler, 2001. "Forecasting Bankruptcy More Accurately: A Simple Hazard Model," The Journal of Business, University of Chicago Press, vol. 74(1), pages 101-24, January.
  3. Grunert, Jens & Norden, Lars & Weber, Martin, 2005. "The role of non-financial factors in internal credit ratings," Journal of Banking & Finance, Elsevier, vol. 29(2), pages 509-531, February.
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