A Comparitive Study of Support Vector Machine and Logistic Regression in Credit Scorecard Model

10.6186/IJIMS.2015.26.4.6

Kiruthika；Dilsha M

Credit scorecard ； logistic regression ； support vector machine ； radial basis function

International Journal of Information and Management Sciences

26卷4期（2015 / 12 / 01）

411 - 422

英文

Credit analysts generally assess the risk of credit applications based on their previous experience. They frequently employ quantitative methods to this end. Most of the financial and banking institutions are using logistic regression to build a credit scorecard. Among the new method, Support Vector Machines (SVM) has been applied in various studies of scorecard modelling. SVM classification is currently an active research area and successfully solves classification problems in many domains. This paper uses standard logistic regression models and compares them with the more advanced least squares support vector machine models with linear and radial basis function kernels. A microfinance data set is used to test the model performance.

1. Hsu, C. W., Chang, C. C. and Lin, C. J. (2003). A practical guide to support vector classification, Available from http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf.
2. Brill, J.(1998).The importance of credit scoring models in improving cash flow and collection.Business Credit,100(1),16-17.
3. Chen, L. H.,Mu-Chen, C.,Chieh, J. W.(2007).Credit scoring with a data mining approach based on support vector machines.Expert Systems with Applications,33,847-856.
4. Chen, M. C.,Huang, S. H.(2003).Credit scoring and rejected instances reassigning through evolutionary computation techniques.Expert Systems with Applications,24(4),433-441.
5. Chen, S. Y.,Liu, X.(2004).The contribution of data mining to information science.Journal of Information Science,30(6),550-558.
6. Cristianini, N.,Shawe-Taylor, J.(2000).An Introduction to support VectorMachines.Cambridge:Cambridge University Press.
7. Davis, R. H.,Edelman, D. B.,Gammerman, A. J.(1992).Machine learning algorithms for credit-card applications.Journal of Mathematics Applied in Business and Industry,4,43-51.
8. Desai, V. S.,Crook, J. N.,Overstreet, G. A.(1996).A comparison of neural networks and linear scoring models in the credit union environment.European Journal of Operational Research,95(1),24-37.
9. Frohlich, H.,Chapelle, O.(2003).Feature selection for support vector machines by means of genetic algorithms.Proceedings of the 15th IEEE international conference on tools with artificial intelligence,Sacramento, California, USA:
10. Henley, W. E.(1995).Milton Keynes, UK,The Open University.
11. Henley, W. E.,Hand, D. J.(1996).A k-nearest neighbor classifier for assessing consumer credit risk.Statistician,44(1),77-95.
12. Hsieh, N.-C.(2005).Hybrid mining approach in the design of credit scoring models.Expert Systems with Applications,28(4),655-665.
13. Hsu, C. W.,Lin, C. J.(2002).A simple decomposition method for support vector machine.Machine Learning,46(1-3),219-314.
14. Huang, Z.,Chen, H.,Hsu, C.-J.,Chen, W.-H.,Wu, S.(2004).Credit rating analysis with sup-port vector machines and neural networks: a market comparative study.Decision Support Systems,37(4),543-558.
15. Joachims, T.(1998).Text categorization with support vector machines.Proceedings of European conference on machine learning (ECML),Chemintz, DE:
16. Kecman, V.(2001).Learning and Soft Computing.Cambridge, MA:The MIT Press.
17. Lee, T.-S.,Chen, I.-F.(2005).A two-stage hybrid credit scoring model using artificial neural networks and multivariate adaptive regression splines.Expert Systems with Applications,28(4),743-752.
18. Lee, T.-S.,Chiu, C.-C.,Lu, C.-J.,Chen, I.-F.(2002).Credit scoring using the hybrid neural discriminant technique.Expert Systems with Applications,23(3),245-254.
19. Malhotra, R.,Malhotra, D. K.(2002).Differentiating between good credits and bad credits using neuro-fuzzy systems.European Journal of Operational Research,136(1),190-211.
20. Pontil, M.,Verri, A.(1998).Support vector machines for 3D object recognition.IEEE Transactions on Pattern Analysis and Machine Intelligence,20(6),637-646.
21. Reichert, A. K.,Cho, C. C.,Wagner, G. M.(1983).An examination of the conceptual issues involved in developing credit-scoring models.Journal of Business and Economic Statistics,1(2),101-114.
22. Salzberg, S. L.(1997).On comparing classifiers: pitfalls to avoid and a recommended approach.Data Mining and Knowledge Discovery,1,317-327.
23. Scholkopf, B.,Smola, A. J.(2000).Statistical Learning and Kernel Methods.Cambridge, MA:MIT Press.
24. Tam, K. Y.,Kiang, M. Y.(1992).Managerial applications of the neural networks: the case of bank failure prediction.Management Science,38(7),926-947.
25. Thomas, L. C.(2000).A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers.International Journal of Forecasting,16(2),149-172.
26. Vapnik, V. N.(1995).The Nature of Statistical Learning Theory.New York:Springer-Verlag.
27. West, D.(2000).Neural network credit scoring models.Computers and Operations Research,27(11-12),1131-1152.
28. Yu, G. X.,Ostrouchov, G.,Geist, A.,Samatova, N. F.(2003).An SVM-based algorithm for iden-tification of photosynthesis-specific genome features.2nd IEEE computer society bioinformatics conference,CA, USA:
29. Zhang, G. P.(2000).Neural networks for classification: a survey.IEEE Transactions on Systems, Man, and Cybernetics-Part C: Applications and Reviews,30(4),451-462.