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Specification and Informational Issues in Credit Scoring

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  • Kiefer, Nicholas M.

    (Cornell U and US Department of the Treasury)

  • Larson, C. Erik

    (Fannie Mae)

Abstract

Lenders use rating and scoring models to rank credit applicants on their expected performance. The models and approaches are numerous. We explore the possibility that estimates generated by models developed with data drawn solely from extended loans are less valuable than they should be because of selectivity bias. We investigate the value of "reject inference"--methods that use a rejected applicant's characteristics, rather than loan performance data, in scoring model development. In the course of making this investigation, we also discuss the advantages of using parametric as well as nonparametric modeling. These issues are discussed and illustrated in the context of a simple stylized model.

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Bibliographic Info

Paper provided by Cornell University, Center for Analytic Economics in its series Working Papers with number 06-11.

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Date of creation: Oct 2006
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Handle: RePEc:ecl:corcae:06-11

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References

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  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.
  2. Crook, Jonathan & Banasik, John, 2004. "Does reject inference really improve the performance of application scoring models?," Journal of Banking & Finance, Elsevier, vol. 28(4), pages 857-874, April.
  3. Boyes, William J. & Hoffman, Dennis L. & Low, Stuart A., 1989. "An econometric analysis of the bank credit scoring problem," Journal of Econometrics, Elsevier, vol. 40(1), pages 3-14, January.
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Cited by:
  1. Glennon, Dennis & Kiefer, Nicholas M. & Larson, C. Erik & Choi, Hwan-sik, 2007. "Development and Validation of Credit-Scoring Models," Working Papers 07-12, Cornell University, Center for Analytic Economics.

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