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Scoring bank loans that may go wrong: a case study

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  • J. S. Cramer

Abstract

A bank employs logistic regression with state‐dependent sample selection to identify loans that may go wrong. The data consist of some 20 000 loans for which a number of conventional accounting ratios of the debtor firm are known; after two years just over 600 have gone wrong. Inspection shows that the state‐dependent sampling technique does not work because the data do not satisfy the standard logit model. Several variants on this model are considered, and it is found that a bounded logit with a ceiling of (far) less than 1 fits the data better. When it comes to their performance in an independent data‐set, however, the differences between the various methods of analysis are negligible.

Suggested Citation

  • J. S. Cramer, 2004. "Scoring bank loans that may go wrong: a case study," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 58(3), pages 365-380, August.
    • Handle: RePEc:bla:stanee:v:58:y:2004:i:3:p:365-380
    DOI: 10.1111/j.1467-9574.2004.00127.x
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    Cited by:

    1. Ha-Thu Nguyen, 2015. "How is credit scoring used to predict default in China?," EconomiX Working Papers 2015-1, University of Paris Nanterre, EconomiX.
    2. Annemiek Vuren & Daniel Vuuren, 2007. "Financial Incentives in Disability Insurance in the Netherlands," De Economist, Springer, vol. 155(1), pages 73-98, March.
    3. J.S. Cramer, 2005. "Omitted Variables and Misspecified Disturbances in the Logit Model," Tinbergen Institute Discussion Papers 05-084/4, Tinbergen Institute.
    4. Medema, Lydian & Koning, Ruud H. & Lensink, Robert, 2009. "A practical approach to validating a PD model," Journal of Banking & Finance, Elsevier, vol. 33(4), pages 701-708, April.
    5. Annemiek Vuren & Daniel Vuuren, 2007. "Financial Incentives in Disability Insurance in the Netherlands," De Economist, Springer, vol. 155(1), pages 73-98, March.
    6. Hussein A. Abdou & John Pointon, 2011. "Credit Scoring, Statistical Techniques And Evaluation Criteria: A Review Of The Literature," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 18(2-3), pages 59-88, April.

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