Credit scoring using the clustered support vector machine

Expert Systems with Applications

Volumn 42, Issue 2, 2015, Pages 741-750

  Harris, Terry ^a  

^a UNIVERSITY OF THE WEST INDIES   (Jamaica)

Author keywords

Clustered support vector machine; Credit risk; Credit scoring; Support vector machine

Indexed keywords

RISK ASSESSMENT; VECTORS;

CLASSIFICATION PERFORMANCE; CREDIT RISKS; CREDIT SCORING; HIGHLY ACCURATE; NONLINEAR APPROACH; NONLINEAR SUPPORT VECTOR MACHINES;

SUPPORT VECTOR MACHINES;

EID: 84907481786     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2014.08.029     Document Type: Article

References (51)

1. Abdou, H. A. H. (2009). Credit scoring models for Egyptian banks: Neural nets and genetic programming versus conventional techniques (Ph.D. Thesis). The University of Plymouth, UK.
2. H.A. Abdou, and J. Pointon Credit scoring and decision making in Egyptian public sector banks International Journal of Managerial Finance 5 4 2009 391 406
3. H.A. Abdou, and J. Pointon Credit scoring, statistical techniques and evaluation criteria: A review of the literature Intelligent Systems in Accounting, Finance and Management 18 2-3 2011 59 88
4. E.I. Altman Financial ratios, discriminant analysis and the prediction of corporate bankruptcy The Journal of Finance 23 4 1968 589 609
5. E.I. Altman Financial ratios, discriminant analysis and the prediction of corporate bankruptcy The Journal of Finance 23 4 1986 589 609
6. G. Arminger, D. Enache, and T. Bonne Analyzing credit risk data: A comparison of logistic discrimination, classification tree analysis, and feedforward networks Computational Statistics 12 2 1997
7. B. Baesens, T. van Gestel, S. Viaene, M. Stepanova, J. Suykens, and J. Vanthienen Benchmarking state-of-the-art classification algorithms for credit scoring Journal of the Operational Research Society 54 2003 1082 1088
8. T. Bellotti, and J. Crook Support vector machines for credit scoring and discovery of significant features Expert Systems with Applications 36 2 2009 3302 3308
9. BGFRS (2013). Consumer Credit Release Board of Governors of the Federal Reserve Systemo. Document Number).
10. C.M. Bishop Neural networks for pattern recognition 1995 Oxford university press
11. A. Bordes, S. Ertekin, J. Weston, and L.O. Bottou Fast kernel classifiers with online and active learning The Journal of Machine Learning Research 6 2005 1579 1619
12. L. Breiman, J.H. Friedman, R.A. Olshen, and C.J. Stone Classification and regression trees 1984 Wadsworth & Brooks Monterey, CA
13. Coffman, J. Y. (1986). The proper role of tree analysis in forecasting the risk behavior of borrowers. Management Decision Systems, Atlanta, MDS Reports, 3(4), 7.
14. C. Cortes, and V. Vapnik Support-vector networks Machine Learning 20 3 1995 273 297
15. J.N. Crook, D.B. Edelman, and L.C. Thomas Recent developments in consumer credit risk assessment European Journal of Operational Research 183 3 2007 1447 1465
16. V.S. Desai, J.N. Crook, and G.A. Overstreet Jr. A comparison of neural networks and linear scoring models in the credit union environment European Journal of Operational Research 95 1 1996 24 37
17. D. Durand Risk elements in consumer instalment financing 1941 National Bureau of Economic Research NY
18. R.A. Eisenbeis Problems in applying discriminant analysis in credit scoring models Journal of Banking & Finance 2 3 1978 205 219
19. P. Falbo Credit-scoring by enlarged discriminant models Omega 19 4 1991 275 289
20. R.A. Fisher The use of multiple measurements in taxonomic problems Annals of Human Genetics 7 2 1936 179 188
21. E. Gately Neural networks for financial forecasting: Top techniques for designing and applying the latest trading systems 1995 John Wiley & Sons, Inc
22. W. Greene Sample selection in credit-scoring models Japan and the World Economy 10 3 1998 299 316
23. Gu, Q., & Han, J. (2013). Clustered support vector machines. In Paper presented at the proceedings of the sixteenth international conference on artificial intelligence and statistics.
24. D.J. Hand Classifier technology and the illusion of progress Statistical science 21 1 2006 1 14
25. Hand, D. J., & Jacka, S. D. (1998). Statistics in finance. Arnold London.
26. D.J. Hand, and W.E. Henley Statistical classification methods in consumer credit scoring: A review Journal of the Royal Statistical Society: Series A (Statistics in Society) 160 3 1997 523 541
27. D.J. Hand, J.J. Oliver, and A.D. Lunn Discriminant analysis when the classes arise from a continuum Pattern Recognition 31 5 1998 641 650
28. D.J. Hand, S.Y. Sohn, and Y. Kim Optimal bipartite scorecards Expert Systems with Applications 29 3 2005 684 690
29. T. Harris Quantitative credit risk assessment using support vector machines: Broad versus Narrow default definitions Expert Systems with Applications 40 11 2013 4404 4413
30. W.E. Henley Statistical aspects of credit scoring 1994 Open University
31. D.W. Hosmer, and S. Lemeshow The multiple logistic regression model Applied Logistic Regression 1 1989 25 37
32. C.L. Huang, M.C. Chen, and C.J. Wang Credit scoring with a data mining approach based on support vector machines Expert Systems with Applications 33 4 2007 847 856
33. Irwin, G. W., Warwick, K., & Hunt, K. J. (1995). Neural network applications in control No. 53. Iet.
34. N. Jentzsch The economics and regulation of financial privacy [electronic resource]: An international comparison of credit reporting systems 2006 Springer
35. K.J. Leonard The development of credit scoring quality measures for consumer credit applications International Journal of Quality & Reliability Management 12 4 1995 79 85
36. T. Masters Advanced algorithms for neural networks: A C++ sourcebook 1995 John Wiley & Sons, Inc
37. E. Mays Handbook of credit scoring 2001 The Glenlake Publishing Company Chicago, IL
38. Y.E. Orgler A credit scoring model for commercial loans Journal of Money, Credit and Banking 2 4 1970 435 445
39. M. Paliwal, and U.A. Kumar Neural networks and statistical techniques: A review of applications Expert Systems with Applications 36 1 2009 2 17
40. J. Platt Fast training of support vector machines using sequential minimal optimization Advances in kernel methods support vector learning 1999 AJ, MIT Press Cambridge, MA 185 208
41. Raiffa, H., & Schlaifer, R. (1961). Applied statistical decision theory, Harvard Business School Publications.
42. E. Rosenberg, and A. Gleit Quantitative methods in credit management: A survey Operations Research 42 4 1994 589 613
43. Sarlija, N., Bensic, M., & Bohacek, Z. (2004). Multinomial model in consumer credit scoring. In Paper presented at the 10th international conference on operational research KOI 2004.
44. A. Saunders, and L.(Allen Credit risk measurement: New approaches to value at risk and other paradigms 1998 John Wiley and Sons New York (March, 2002)
45. N. Siddiqi Credit risk scorecards: Developing and implementing intelligent credit scoring Vol. 3 2005 Wiley.com
46. K.A. Smith, and J.N.D. Gupta Neural networks in business: Techniques and applications IGI Global 2003
47. A. Steenackers, and M.J. Goovaerts A credit scoring model for personal loans Insurance: Mathematics and Economics 8 1 1989 31 34
48. Z. Wang, S.C. Yan, and C.S. Zhang Active learning with adaptive regularization Pattern Recognition 44 10-11 2011 2375 2383
49. L. Yu Bio-inspired credit risk analysis: Computational intelligence with support vector machines 2008 Springer
50. Zekic-Susac, M., Sarlija, N., & Bensic, M. (2004). Small business credit scoring: A comparison of logistic regression, neural network, and decision tree models. In Paper presented at the 26th international conference on information technology interfaces, 2004.
51. L. Zhou, K.K. Lai, and L. Yu Least squares support vector machines ensemble models for credit scoring Expert Systems with Applications 37 1 2010 127 133