Credit Risk Analysis Using a Reliability-Based Neural Network Ensemble Model

  • Kin Keung Lai
  • Lean Yu
  • Shouyang Wang
  • Ligang Zhou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4132)


Credit risk analysis is an important topic in the financial risk management. Due to recent financial crises and regulatory concern of Basel II, credit risk analysis has been the major focus of financial and banking industry. An accurate estimation of credit risk could be transformed into a more efficient use of economic capital. In this study, we try to use a triple-phase neural network ensemble technique to design a credit risk evaluation system to discriminate good creditors from bad ones. In this model, many diverse neural network models are first created. Then an uncorrelation maximization algorithm is used to select the appropriate ensemble members. Finally, a reliability-based method is used for neural network ensemble. For further illustration, a publicly credit dataset is used to test the effectiveness of the proposed neural ensemble model.


Support Vector Machine Credit Risk Ensemble Member Ensemble Model Back Propagation 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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Wang, Y.Q., Wang, S.Y., Lai, K.K.: A New Fuzzy Support Vector Machine to Evaluate Credit Risk. IEEE Transactions on Fuzzy Systems 13, 820–831 (2005)CrossRefGoogle Scholar
  2. 2.
    Fisher, R.A.: The Use of Multiple Measurements in Taxonomic Problems. Annals of Eugenics 7, 179–188 (1936)Google Scholar
  3. 3.
    Wiginton, J.C.: A Note on the Comparison of Logit and Discriminant Models of Consumer Credit Behaviour. Journal of Financial Quantitative Analysis 15, 757–770 (1980)CrossRefGoogle Scholar
  4. 4.
    Grablowsky, B.J., Talley, W.K.: Probit and Discriminant Functions for Classifying Credit Applicants: A Comparison. Journal of Economic Business 33, 254–261 (1981)Google Scholar
  5. 5.
    Glover, F.: Improved Linear Programming Models for Discriminant Analysis. Decision Science 21, 771–785 (1990)CrossRefGoogle Scholar
  6. 6.
    Mangasarian, O.L.: Linear and Nonlinear Separation of Patterns by Linear Programming. Operations Research 13, 444–452 (1965)MATHCrossRefMathSciNetGoogle Scholar
  7. 7.
    Henley, W.E., Hand, D.J.: A k-NN Classifier for Assessing Consumer Credit Risk. Statistician 45, 77–95 (1996)CrossRefGoogle Scholar
  8. 8.
    Makowski, P.: Credit Scoring Branches out. Credit World 75, 30–37 (1985)Google Scholar
  9. 9.
    Malhotra, R., Malhotra, D.K.: Evaluating Consumer Loans Using Neural Networks. Omega 31, 83–96 (2003)CrossRefGoogle Scholar
  10. 10.
    Smalz, R., Conrad, M.: Combining Evolution with Credit Apportionment: A New Learning Algorithm for Neural Nets. Neural Networks 7, 341–351 (1994)CrossRefGoogle Scholar
  11. 11.
    Chen, M.C., Huang, S.H.: Credit Scoring and Rejected Instances Reassigning through Evolutionary Computation Techniques. Expert Systems with Applications 24, 433–441 (2003)MATHCrossRefGoogle Scholar
  12. 12.
    Varetto, F.: Genetic Algorithms Applications in the Analysis of Insolvency Risk. Journal of Banking and Finance 22, 1421–1439 (1998)CrossRefGoogle Scholar
  13. 13.
    Van Gestel, T., Baesens, B., Garcia, J., Van Dijcke, P.: A Support Vector Machine Approach to Credit Scoring. Bank en Financiewezen 2, 73–82 (2003)Google Scholar
  14. 14.
    Huang, Z., Chen, H.C., Hsu, C.J., Chen, W.H., Wu, S.S.: Credit Rating Analysis with Support Vector Machines and Neural Networks: A Market Comparative Study. Decision Support Systems 37, 543–558 (2004)CrossRefGoogle Scholar
  15. 15.
    Piramuthu, S.: Financial Credit-Risk Evaluation with Neural and Neurofuzzy Systems. European Journal of Operational Research 112, 310–321 (1999)CrossRefGoogle Scholar
  16. 16.
    Malhotra, R., Malhotra, D.K.: Differentiating between Good Credits and Bad Credits Using Neuro-Fuzzy Systems. European Journal of Operational Research 136, 190–211 (2002)MATHCrossRefGoogle Scholar
  17. 17.
    Thomas, L.C.: A Survey of Credit and Behavioral Scoring: Forecasting Financial Risk of Lending to Consumers. International Journal of Forecasting 16, 149–172 (2002)CrossRefGoogle Scholar
  18. 18.
    Thomas, L.C., Oliver, R.W., Hand, D.J.: A Survey of the Issues in Consumer Credit Modelling Research. Journal of the Operational Research Society 56, 1006–1015 (2005)MATHCrossRefGoogle Scholar
  19. 19.
    Hornik, K., Stinchocombe, M., White, H.: Multilayer Feedforward Networks are Universal Approximators. Neural Networks 2, 359–366 (1989)CrossRefGoogle Scholar
  20. 20.
    White, H.: Connectionist Nonparametric Regression: Multilayer Feedforward Networks Can Learn Arbitrary Mappings. Neural Networks 3, 535–549 (1990)CrossRefGoogle Scholar
  21. 21.
    Yang, S., Browne, A.: Neural Network Ensembles: combining multiple models for enhanced performance using a multistage approach. Expert Systems 21, 279–288 (2004)CrossRefGoogle Scholar
  22. 22.
    Yu, L., Lai, K.K., Wang, S.Y., Huang, W.: A Bias-Variance -Complexity Trade-off Framework for Complex System Modeling. In: Gavrilova, M.L., Gervasi, O., Kumar, V., Tan, C.J.K., Taniar, D., Laganá, A., Mun, Y., Choo, H. (eds.) ICCSA 2006. LNCS, vol. 3980, pp. 518–527. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  23. 23.
    Yu, L., Wang, S.Y., Lai, K.K.: A Novel Nonlinear Ensemble Forecasting Model Incorporating GLAR and ANN for Foreign Exchange Rates. Computers and Operations Research 32, 2523–2541 (2005)MATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Kin Keung Lai
    • 1
    • 2
  • Lean Yu
    • 2
    • 3
  • Shouyang Wang
    • 1
    • 3
  • Ligang Zhou
    • 2
  1. 1.College of Business AdministrationHunan UniversityChangshaChina
  2. 2.Department of Management SciencesCity University of Hong KongKowloon, Hong Kong
  3. 3.Institute of Systems ScienceAcademy of Mathematics and Systems Science, Chinese Academy of SciencesBeijingChina

Personalised recommendations