Assessment of Financial Risk Prediction Models with Multi-criteria Decision Making Methods

  • Jose Salvador Sánchez
  • Vicente García
  • Ana Isabel Marqués
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7664)


A wide range of classification models have been explored for financial risk prediction, but conclusions on which technique behaves better may vary when different performance evaluation measures are employed. Accordingly, this paper proposes the use of multiple criteria decision making tools in order to give a ranking of algorithms. More specifically, the selection of the most appropriate credit risk prediction method is here modeled as a multi-criteria decision making problem that involves a number of performance measures (criteria) and classification techniques (alternatives). An empirical study is carried out to evaluate the performance of ten algorithms over six real-life credit risk data sets. The results reveal that the use of a unique performance measure may lead to unreliable conclusions, whereas this situation can be overcome by the application of multi-criteria decision making techniques.


Classification model Financial risk prediction Multi-criteria decision making methods 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Thomas, L.C., Edelman, D.B., Crook, J.N.: Credit Scoring and Its Applications. SIAM, Philadelphia (2002)zbMATHCrossRefGoogle Scholar
  2. 2.
    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. Decis. Supp. Syst. 37(4), 543–558 (2004)CrossRefGoogle Scholar
  3. 3.
    Desai, V.S., Crook, J.N., Overstreet, G.A.: A Comparison of Neural Networks and Linear Scoring Models in the Credit Union Environment. Europ. J. Operat. Research 95(1), 24–37 (1996)zbMATHCrossRefGoogle Scholar
  4. 4.
    Yobas, M.B., Crook, J.N., Ross, P.: Credit Scoring Using Neural and Evolutionary Techniques. IMA J. Math. Appl. Business Industry 11(4), 111–125 (2000)MathSciNetzbMATHGoogle 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. J. Operat. Research Society 54(6), 627–635 (2003)zbMATHCrossRefGoogle Scholar
  6. 6.
    Bensic, M., Sarlija, N., Zekic-Susac, M.: Modelling Small-Business Credit Scoring by Using Logistic Regression, Neural Networks and Decision Trees. Intell. Syst. Account. Finance Manag. 13(3), 133–150 (2005)CrossRefGoogle Scholar
  7. 7.
    Antonakis, A.C., Sfakianakis, M.E.: Assessing naïve Bayes as a Method for Screening Credit Applicants. J. Appl. Stat. 36(5), 537–545 (2009)MathSciNetzbMATHCrossRefGoogle Scholar
  8. 8.
    Wang, G., Hao, J., Ma, J., Jiang, H.: A Comparative Assessment of Ensemble Learning for Credit Scoring. Expert Syst. Appl. 38(1), 223–230 (2011)CrossRefGoogle Scholar
  9. 9.
    Hand, D.J.: Good Practice in Retail Credit Scorecard Assessment. J. Operat. Research Society 56(9), 1109–1117 (2005)zbMATHCrossRefGoogle Scholar
  10. 10.
    Abdou, H.A., Pointon, J.: Credit Scoring, Statistical Techniques and Evaluation Criteria: A Review of the Literature. Intell. Syst. Account. Finance & Manag. 18(2-3), 59–88 (2011)CrossRefGoogle Scholar
  11. 11.
    Lee, J.S., Zhu, D.: When Costs Are Unequal and Unknown: A Subtree Grafting Approach for Unbalanced Data Classification. Decis. Sci. 42(4), 803–829 (2011)CrossRefGoogle Scholar
  12. 12.
    Sokolova, M., Lapalme, G.: A Systematic Analysis of Performance Measures for Classification Tasks. Inform. Process. Manag. 45(4), 427–437 (2009)CrossRefGoogle Scholar
  13. 13.
    Köksalan, M., Wallenius, J., Zionts, S.: Multiple Criteria Decision Making: From Early History to the 21st Century. World Scientific, Singapore (2011)CrossRefGoogle Scholar
  14. 14.
    Hwang, C.L., Yoon, K.: Multiple Attribute Decision Making – Methods and Applications. Springer, New York (1981)zbMATHCrossRefGoogle Scholar
  15. 15.
    Brans, J.P., Vincke, P.H.: A Preference Ranking Organisation Method: The PROMETHEE Method for Multiple Criteria Decision-Making. Manag. Sci. 31(6), 647–656 (1985)MathSciNetzbMATHCrossRefGoogle Scholar
  16. 16.
    Sabzevari, H., Soleymani, M., Noorbakhsh, E.: A Comparison between Statistical and Data Mining Methods for Credit Scoring in Case of Limited Available Data. In: Proceedings of the 3rd CRC Credit Scoring Conference, Edinburgh, UK (2007)Google Scholar
  17. 17.
    Pietruszkiewicz, W.: Dynamical Systems and Nonlinear Kalman Filtering Applied in Classification. In: Proceedings of the 7th IEEE International Conference on Cybernetic Intelligent Systems, London, UK, pp. 263–268 (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Jose Salvador Sánchez
    • 1
  • Vicente García
    • 1
  • Ana Isabel Marqués
    • 2
  1. 1.Dep. Computer Languages and Systems – Institute of New Imaging TechnologiesUniversitat Jaume ICastelló de la PlanaSpain
  2. 2.Dep. Business Administration and MarketingUniversitat Jaume ICastelló de la PlanaSpain

Personalised recommendations