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Bayesian Methods for Improving Credit Scoring Models

  • Posch Peter N.

    (University of Ulm)

  • Loeffler Gunter

    (University of Ulm)

  • Schoene Christiane

    (University of Ulm)

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://econwpa.repec.org/eps/fin/papers/0505/0505024.pdf
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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
Contact details of provider: Web page: http://econwpa.repec.org

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  1. 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.
  2. Zellner, Arnold & Rossi, Peter E., 1984. "Bayesian analysis of dichotomous quantal response models," Journal of Econometrics, Elsevier, vol. 25(3), pages 365-393, July.
  3. 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.
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