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Assessing the discriminatory power of credit scores

Author

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  • Kraft, Holger
  • Kroisandt, Gerald
  • Müller, Marlene

Abstract

We discuss how to assess the performance for credit scores under the assumption that for credit data only a part of the defaults and nondefaults is observed. The paper introduces a criterion that is based on the difference of the score distributions under default and nondefault. We show how to estimate bounds for this criterion, the Gini coefficient and the accuracy ratio.

Suggested Citation

  • Kraft, Holger & Kroisandt, Gerald & Müller, Marlene, 2002. "Assessing the discriminatory power of credit scores," SFB 373 Discussion Papers 2002,67, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    • Handle: RePEc:zbw:sfb373:200267
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    File URL: https://www.econstor.eu/bitstream/10419/65361/1/727044613.pdf
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    References listed on IDEAS

    as
    1. D. J. Hand & W. E. Henley, 1997. "Statistical Classification Methods in Consumer Credit Scoring: a Review," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 160(3), pages 523-541, September.
    2. Horowitz, Joel L. & Manski, Charles F., 1998. "Censoring of outcomes and regressors due to survey nonresponse: Identification and estimation using weights and imputations," Journal of Econometrics, Elsevier, vol. 84(1), pages 37-58, May.
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    Cited by:

    1. Izabela Majer, 2006. "Application scoring: logit model approach and the divergence method compared," Working Papers 17, Department of Applied Econometrics, Warsaw School of Economics.

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    More about this item

    Keywords

    credit rating; credit score; discriminatory power; sample selection; Gini coefficient; accuracy ratio;
    All these keywords.

    JEL classification:

    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

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