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Specification and Informational Issues in Credit Scoring

Author

Listed:
  • 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.

Suggested Citation

  • Kiefer, Nicholas M. & Larson, C. Erik, 2006. "Specification and Informational Issues in Credit Scoring," Working Papers 06-11, Cornell University, Center for Analytic Economics.
    • Handle: RePEc:ecl:corcae:06-11
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    File URL: https://cae.economics.cornell.edu/06-11.pdf
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    References listed on IDEAS

    as
    1. James J. Heckman, 1976. "The Common Structure of Statistical Models of Truncation, Sample Selection and Limited Dependent Variables and a Simple Estimator for Such Models," NBER Chapters, in: Annals of Economic and Social Measurement, Volume 5, number 4, pages 475-492, National Bureau of Economic Research, Inc.
    2. 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.
    3. Harold Bierman, Jr. & Warren H. Hausman, 1970. "The Credit Granting Decision," Management Science, INFORMS, vol. 16(8), pages 519-532, April.
    4. 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.
    5. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

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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.
    2. Ha-Thu Nguyen, 2016. "Reject inference in application scorecards: evidence from France," EconomiX Working Papers 2016-10, University of Paris Nanterre, EconomiX.
    3. Ha Thu Nguyen, 2016. "Reject inference in application scorecards: evidence from France," Working Papers hal-04141601, HAL.
    4. Adrien Ehrhardt & Christophe Biernacki & Vincent Vandewalle & Philippe Heinrich & S'ebastien Beben, 2019. "R\'eint\'egration des refus\'es en Credit Scoring," Papers 1903.10855, arXiv.org.

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    More about this item

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

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