Non-parametric Statistical Analysis of Machine Learning Methods for Credit Scoring

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 171)

Abstract

Various machine learning techniques have been explored for credit scoring and management, but no consistent conclusions have been drawn on which method shows the best behaviour. This paper presents an experimental analysis involving five real-world databases with several credit scoring models, including logistic regression, neural networks, support vector machines, decision trees, rule induction algorithms, Bayesian models, k nearest neighbours decision rule, and classifier ensembles. Particularly, we analyse the performance of this set of algorithms by means of a non-parametric statistical test and two post-hoc procedures for making pairwise comparisons.

Keywords

Support Vector Machine Radial Basis Function Random Forest Credit Risk Radial Basis Function Neural Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Abdou, H.A.: An evaluation of alternative scoring models in private banking. The Journal of Risk Finance 10(1), 38–53 (2009)CrossRefGoogle Scholar
  2. 2.
    Abrahams, C.R., Zhang, M.: Fair Lending Compliance: Intelligence and Implications for Credit Risk Management. Wiley, Hoboken (2008)Google Scholar
  3. 3.
    Alpaydin, E.: Introduction to Machine Learning. MIT Press, Cambridge (2010)MATHGoogle Scholar
  4. 4.
    Altman, E.I.: Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance 23(4), 589–611 (1968)CrossRefGoogle Scholar
  5. 5.
    Baesens, B., Gestel, T.V., Viaene, S., Stepanova, M., Suykens, J., Vanthienen, J.: Benchmarking state-of-the-art classification algorithms for credit scoring. Journal of the Operational Research Society 54(6), 627–635 (2003)CrossRefMATHGoogle Scholar
  6. 6.
    Bellotti, T., Crook, J.N.: Support vector machines for credit scoring and discovery of significant features. Expert Systems with Applications 36(2), 3302–3308 (2009)CrossRefGoogle Scholar
  7. 7.
    Bensic, M., Sarlija, N., Zekic-Susac, M.: Modelling small-business credit scoring by using logistic regression, neural networks and decision trees. Intelligent Systems in Accounting, Finance and Management 13(3), 133–150 (2005)CrossRefGoogle Scholar
  8. 8.
    Demšar, J.: Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research 7(1), 1–30 (2006)MATHGoogle Scholar
  9. 9.
    Desai, V.S., Crook, J.N., Overstreet, G.A.: A comparison of neural networks and linear scoring models in the credit union environment. European Journal of Operational Research 95(1), 24–37 (1996)CrossRefMATHGoogle Scholar
  10. 10.
    Elkan, C.: The foundations of cost-sensitive learning. In: Proc. 17th Intl. Joint Conf. Artificial Intelligence, Seattle, WA, pp. 973–978 (2001)Google Scholar
  11. 11.
    Elsayad, A.M.: Implementing automated prediction systems for credit scoring. ICGST International Journal on Automatic Control and Systems Engineering 10(1), 11–19 (2010)Google Scholar
  12. 12.
    Ferri, C., Hernández-Orallo, J., Modroiu, R.: An experimental comparison of performance measures for classification. Pattern Recognition Letters 30(1), 27–38 (2009)CrossRefGoogle Scholar
  13. 13.
    Frank, A., Asuncion, A.: UCI Machine Learning Database Repository (2010), http://archive.ics.uci.edu/ml
  14. 14.
    Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. SIGKDD Explorations Newsletter 11(1), 10–18 (2009)CrossRefGoogle Scholar
  15. 15.
    Hand, D.J.: Good practice in retail credit scorecard assessment. Journal of the Operational Research Society 56(9), 1109–1117 (2005)CrossRefMATHGoogle Scholar
  16. 16.
    Huang, Z., Chen, H., Hsu, C.J., Chen, W.H., Wu, S.: Credit rating analysis with support vector machines and neural networks: A market comparative study. Decision Support Systems 37(4), 543–558 (2004)CrossRefGoogle Scholar
  17. 17.
    Japkowicz, N., Shah, M.: Evaluating Learning Algorithms: A Classification Perspective. Cambridge University Press, New York (2011)CrossRefMATHGoogle Scholar
  18. 18.
    Lee, T.S., Chen, I.F.: A two-stage hybrid credit scoring model using artificial neural networks and multivariate adaptive regression splines. Expert Systems with Applications 28(4), 743–752 (2005)CrossRefGoogle Scholar
  19. 19.
    Pietruszkiewicz, W.: Dynamical systems and nonlinear Kalman filtering applied in classification. In: Proc. of 7th IEEE International Conference on Cybernetic Intelligent Systems, London, UK, pp. 263–268 (2008)Google Scholar
  20. 20.
    Sabzevari, H., Soleymani, M., Noorbakhsh, E.: A comparison between statistical and data mining methods for credit scoring in case of limited available data. In: Proc. of the 3rd CRC Credit Scoring Conference, Edinburgh, UK (2007)Google Scholar
  21. 21.
    Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures. CRC Press, Boca Raton (2011)Google Scholar
  22. 22.
    Sokolova, M., Lapalme, G.: A systematic analysis of performance measures for classification tasks. Infornation Processing & Management 45(4), 427–437 (2009)CrossRefGoogle Scholar
  23. 23.
    Thomas, L.C., Edelman, D.B., Crook, J.N.: Credit Scoring and Its Applications. SIAM, Philadelphia (2002)CrossRefMATHGoogle Scholar
  24. 24.
    Yang, Z., Wang, Y., Bai, Y., Zhang, X.: Measuring scorecard performance. In: Proc. 4th Intl. Conf. Computational Science, Krakow, Poland, pp. 900–906 (2004)Google Scholar
  25. 25.
    Yobas, M.B., Crook, J.N., Ross, P.: Credit scoring using neural and evolutionary techniques. IMA Journal of Mathematics Applied in Business and Industry 11(4), 111–125 (2000)MathSciNetMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Institute of New Imaging Technologies, Department of Computer Languages and SystemsUniversitat Jaume ICastelló de la PlanaSpain
  2. 2.Department of Business Administration and MarketingUniversitat Jaume ICastelló de la PlanaSpain

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