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Neural Network Metalearning for Credit Scoring

  • Kin Keung Lai
  • Lean Yu
  • Shouyang Wang
  • Ligang Zhou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4113)

Abstract

In the field of credit risk analysis, the problem that we often encountered is to increase the model accuracy as possible using the limited data. In this study, we discuss the use of supervised neural networks as a metalearning technique to design a credit scoring system to solve this problem. First of all, a bagging sampling technique is used to generate different training sets to overcome data shortage problem. Based on the different training sets, the different neural network models with different initial conditions or training algorithms is then trained to formulate different credit scoring models, i.e., base models. Finally, a neural-network-based metamodel can be produced by learning from all base models so as to improve the reliability, i.e., predict defaults accurately. For illustration, a credit card application approval experiment is performed.

Keywords

Neural Network Support Vector Machine Linear Discriminant Analysis Neural Network Model Credit Scoring 
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.
    Lai, K.K., Yu, L., Zhou, L.G., Wang, S.Y.: Credit Risk Evaluation with Least Square Support Vector Machine. LNCS (2006)Google Scholar
  2. 2.
    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
  3. 3.
    Fisher, R.A.: The Use of Multiple Measurements in Taxonomic Problems. Annals of Eugenics 7, 179–188 (1936)Google Scholar
  4. 4.
    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
  5. 5.
    Malhotra, R., Malhotra, D.K.: Evaluating Consumer Loans Using Neural Networks. Omega 31, 83–96 (2003)CrossRefGoogle Scholar
  6. 6.
    Chan, P., Stolfo, S.: Meta-Learning for Multistrategy and Parallel Learning. In: Proceedings of the Second International Workshop on Multistrategy Learning, pp. 150–165 (1993)Google Scholar
  7. 7.
    Breiman, L.: Bagging Predictors. Machine Learning 26, 123–140 (1996)Google Scholar
  8. 8.
    Lai, K.K., Yu, L., 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

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

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