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Application scoring: logit model approach and the divergence method compared

Author

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  • Izabela Majer

    (Warsaw School of Economics)

Abstract

This study presents the example of application scoring. Two methods are considered: logit model approach and the divergence method. The practical example uses contemporary data on loan applications from the Polish bank. The constructed scoring models are validated on the hold-out sample. Both types of models seem to be acceptable and have high discriminatory power. The prediction accuracy measures indicate that the scoring based on divergence method is better than the one founded on logit model approach.

Suggested Citation

  • Izabela Majer, 2006. "Application scoring: logit model approach and the divergence method compared," Working Papers 17, Department of Applied Econometrics, Warsaw School of Economics.
    • Handle: RePEc:wse:wpaper:17
    as

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    File URL: http://kolegia.sgh.waw.pl/pl/KAE/struktura/IE/struktura/ZES/Documents/Working_Papers/aewp10-06.pdf
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    References listed on IDEAS

    as
    1. R M Oliver & E Wells, 2001. "Efficient frontier cutoff policies in credit portfolios," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 52(9), pages 1025-1033, September.
    2. 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.
    3. David Hand & Niall Adams, 2000. "Defining attributes for scorecard construction in credit scoring," Journal of Applied Statistics, Taylor & Francis Journals, vol. 27(5), pages 527-540.
    4. Thomas, Lyn C. & Edelman, David B. & Crook, Jonathan, 2004. "Readings in Credit Scoring: Foundations, Developments, and Aims," OUP Catalogue, Oxford University Press, number 9780198527978.
    5. 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.
    6. Gourieroux,Christian, 2000. "Econometrics of Qualitative Dependent Variables," Cambridge Books, Cambridge University Press, number 9780521589857, January.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    credit scoring; logit model; divergence method; credit risk; classification;
    All these keywords.

    JEL classification:

    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General

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