Skip to main content

An Integrated Data Mining Model for Customer Credit Evaluation

  • Conference paper
Computational Science and Its Applications – ICCSA 2005 (ICCSA 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3482))

Included in the following conference series:

  • 1435 Accesses

Abstract

Based on the customer information relating to details of financing and payment histories from a financial institution, this study derived single data mining models using MLP, MDA, and DTM. The results obtained from these single models were subsequently compared with the results from an integrated model developed using GA. This study not only verifies existing single models and but also attempts to overcome the limitations of these approaches. While our comparative analysis of single models for the purpose of identifying the best-fit model relies upon existing techniques, this study presents a new methodology to build an integrated data mining model using GA.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Boyle, M., Crook, J.N., Hamilton, R., Thomas, L.C.: Methods for Credit Scoring Applied to Slow Payers. In: Thomas, L.C., Crook, J.N., Edelman, D.B. (eds.) Credit Scoring and Credit Control, pp. 75–90. Oxford University Press, Oxford (1992)

    Google Scholar 

  2. Chae, S.: Social Science Research Methodology, 2nd edn. Hakhyunsa, Seoul (1999)

    Google Scholar 

  3. Cheng, B., Titterington, D.M.: Neural Networks-A Review from a Statistical Perspective. Statistical Science 9, 2–30 (1994)

    Article  MATH  MathSciNet  Google Scholar 

  4. Jong-hu, C., Sang-tae, H.: Analysis of a Decision Making Tree for Data Mining Using AnswerTree. SPSS Academy, Seoul (2000)

    Google Scholar 

  5. Desai, V.S., Convay, D.G., Crook, J.N., Overstreet, G.A.: Credit Scoring Models in the Credit Union Environment Using Neural Networks and Genetic Algorithms. IMA Journal of Mathematics Applied in Business and Industry 8, 323–346 (1997)

    MATH  Google Scholar 

  6. Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading (1989)

    MATH  Google Scholar 

  7. Gupta, Y.P., Gupta, M.C., Kumar, A.K., Sundram, C.: Minimizing Total Intercell and Intracell Moves in Cellular Manufacturing: A Genetic Algorithm Approach. INT. J. of Computer Integrated Manufacturing 8(2), 92–101 (1995)

    Article  Google Scholar 

  8. Henley, W.E.: Statistical Aspects of Credit Scoring. PhD Thesis, Open University (1995)

    Google Scholar 

  9. Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)

    Google Scholar 

  10. Hon, K.K.B., Chi, H.: A New Approach of Group Technology Part Families Optimization. Annals of the CIRP 43(1) (1994)

    Google Scholar 

  11. Imielinski, T., Mannila, H.: A Database Perspective on Knowledge Discovery. Communications of the ACM 39(11), 214–225 (1996)

    Article  Google Scholar 

  12. Jain, B.A., Nag, B.N.: Performance Evaluation of Neural Network Decision Models. Journal of Management Information Systems 14(2), 201–216 (1997)

    Google Scholar 

  13. Jeong, C., Choi, I.: A Statistical Analysis using SPSSWIN. Muyok Publishing, Seoul (1998)

    Google Scholar 

  14. Kim, G.: Integrated Data Mining Model for Prediction of Customer Credit Risk in Installment Purchase Financing. Ph.D Dissertation, Catholic University of Daegu (2003)

    Google Scholar 

  15. Kim, H.: Prediction Model for Credit Risk Assessment in Installment Purchase Financing Integrating Several Classifiers through Genetic Algorithm. MA Thesis, Daegu University (2001)

    Google Scholar 

  16. Kim, E., Kim, W., Lee, Y.: Purchase Propensity Prediction of EC Customer by Combining Multiple Classifiers Base on GA. In: Proceedings of International Conference on Electronic Commerce, pp. 274–280 (2000)

    Google Scholar 

  17. Mangasarian, O.L.: Linear and Nonlinear Separation of Patterns by Linear Programming. Operations Research 13, 444–452 (1965)

    Article  MATH  MathSciNet  Google Scholar 

  18. Mehta, D.: The Formulation of Credit Policy Models. Management Science 15, 30–50 (1968)

    Article  Google Scholar 

  19. Srinivasan, V., Kim, Y.H.: The Bierman-Hausman Credit Granting Model-A Note. Management Science 33, 1361–1362 (1987)

    Article  Google Scholar 

  20. Thomas, L.C.: A Survey of Credit and Behavioral Scoring: Forecasting Financial Risk of Lending to Consumers. International Journal of Forecasting 16, 149–172 (2000)

    Article  MATH  Google Scholar 

  21. West, D.: Neural Network Credit Scoring Models. Computers & Operations Research 27, 1131–1152 (2000)

    Article  MATH  Google Scholar 

  22. Wiginton, J.C.: A Note on the Comparison of Logit and Discriminant Models of Consumer Credit Behavior. Journal of Financial and Quantitative Analysis 15, 757–770 (1980)

    Article  Google Scholar 

  23. Wong, B.K., Bodnovich, T.A., Selvi, Y.: Neural Network Applications in Business: A Review and Analysis of the Literature(1988-95). Decision Support Systems 19, 301–320 (1997)

    Article  Google Scholar 

  24. Yobas, M.B., Crook, J.N., Ross, P.: Credit Scoring Using Neural and Evolutionary Techniques. Credit Research Centre, University of Edinburgh, Working Paper (1997)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Kim, K.S., Hwang, H.J. (2005). An Integrated Data Mining Model for Customer Credit Evaluation. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2005. ICCSA 2005. Lecture Notes in Computer Science, vol 3482. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11424857_87

Download citation

  • DOI: https://doi.org/10.1007/11424857_87

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25862-9

  • Online ISBN: 978-3-540-32045-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics