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Enhanced Decision Support in Credit Scoring Using Bayesian Binary Quantile Regression

Author

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  • V. L. MIGUÉIS
  • D. F. BENOIT

    ()

  • D. VAN DEN POEL

    ()

Abstract

Fierce competition as well as the recent financial crisis in financial and banking industries made credit scoring gain importance. An accurate estimation of credit risk helps organizations to decide whether or not to grant credit to potential customers. Many classification methods have been suggested to handle this problem in the literature. This paper proposes a model for evaluating credit risk based on binary quantile regression, using Bayesian estimation. This paper points out the distinct advantages of the latter approach: that is (i) the method provides accurate predictions of which customers may default in the future, (ii) the approach provides detailed insight into the effects of the explanatory variables on the probability of default, and (iii) the methodology is ideally suited to build a segmentation scheme of the customers in terms of risk of default and the corresponding uncertainty about the prediction. An often studied dataset from a German bank is used to show the applicability of the method proposed. The results demonstrate that the methodology can be an important tool for credit companies that want to take the credit risk of their customer fully into account.

Suggested Citation

  • V. L. Miguéis & D. F. Benoit & D. Van Den Poel, 2012. "Enhanced Decision Support in Credit Scoring Using Bayesian Binary Quantile Regression," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/803, Ghent University, Faculty of Economics and Business Administration.
  • Handle: RePEc:rug:rugwps:12/803
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    References listed on IDEAS

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    1. Len Umantsev & Victor Chernozhukov, 2001. "Conditional value-at-risk: Aspects of modeling and estimation," Empirical Economics, Springer, vol. 26(1), pages 271-292.
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    Cited by:

    1. repec:jss:jstsof:v:076:i07 is not listed on IDEAS
    2. Hussein Hashem & Veronica Vinciotti & Rahim Alhamzawi & Keming Yu, 2016. "Quantile regression with group lasso for classification," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 10(3), pages 375-390, September.
    3. repec:spr:svcbiz:v:11:y:2017:i:4:d:10.1007_s11628-016-0332-3 is not listed on IDEAS

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    Keywords

    Credit Scoring; Quantile regression; Classification; Bayesian estimation; Markov Chain Monte Carlo;

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