Behavioral assessment of recoverable credit of retailer's customers

Information Sciences

Volumn 180, Issue 19, 2010, Pages 3703-3717

  Ha, Sung Ho ^a  

^a Kyungpook National University   (South Korea)

Author keywords

Credit prediction; Credit recovery; Delinquency management; Kohonen network; Survival analysis

Indexed keywords

COMPUTER SYSTEM RECOVERY; FORECASTING; HAZARDS;

BEHAVIORAL ASSESSMENT; ECONOMIC DOWNTURN; HOMOGENEOUS GROUP; KOHONEN NETWORK; PREDICTION ACCURACY; PREDICTION MODEL; PROPORTIONAL HAZARD MODELS; SURVIVAL ANALYSIS;

SALES;

EID: 77958511317     PISSN: 00200255     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ins.2010.06.012     Document Type: Article

References (51)

1. H. Abdou, J. Pointon, A. El-Masry, Neural nets versus conventional techniques in credit scoring in Egyptian banking, Expert Systems with Applications 35(2008) 1275-1292.
2. L. Allen, C. DeLong, A. Saunders, Issues in the credit risk modeling of retail markets, Journal of Banking and Finance 28 (2004) 727-752.
3. L.N. Allen, L.C. Rose, Financial survival analysis of defaulted debtors, Journal of the Operational Research Society 57 (2006) 630-636.
4. E.I. Altman, G. Marco, F. Varetto, Corporate distress diagnosis: comparisons using linear discriminant analysis and neural networks (the Italian experience), Journal of Banking and Finance 18 (1994) 505-529.
5. B. Baesens, M. Egmont-Petersen, R. Castelo, J. Vanthienen, Learning Bayesian network classifiers for credit scoring using Markov chain Monte Carlo search, in: Proceedings of the 16th International Conference on Pattern Recognition, Quebec, Canada, 2002, pp. 49-52.
6. B. Baesens, T.V. Gestel, M. Stepanova, D.V. Poel, Neural network survival analysis for personal loan data, Journal of the Operational Research Society 59 (2005) 1089-1098.
7. M.J.A. Berry, C. Linoff, Data Mining Techniques, second ed., Wiley, Indianapolis, IN, 2004.
8. P.S. Bradley, U.M. Fayyad, O.L. Mangasarian, Data mining: overview and optimization opportunities, INFORMS Journal on Computing (1998) 17-22.
9. M. Bruche, C. Gonzalez-Aguado, Recovery rates, default probabilities, and the credit cycle, Journal of Banking and Finance 34 (2009) 754-764.
10. M.-C. Chen, L-S. Chen, C.-C. Hsu, W.-R. Zeng, An information granulation based data mining approach for classifying imbalanced data, Information Sciences 178 (2008) 3214-3227.
11. M.C. Chen, S.H. Huang, Credit scoring and rejected instances reassigning through evolutionary computation techniques, Expert System with Applications 24 (2003) 433-441.
12. W. Chen, C. Ma, L. Ma, Mining the customer credit using hybrid support vector machine technique, Expert Systems with Applications 36 (2009) 7611 7616.
13. B. Cheng, D.M. Titterington, Neural networks: a review from a statistical perspective, Statistical Science 9 (1994) 2-30.
14. D.R. Cox, Regression models and life-tables, Journal of Royal Statistical Society 34 (1972) 187-220.
15. J.N. Crook, D.B. Edelman, L.C. Thomas, Recent developments in consumer credit risk assessment, European Journal of Operational Research 183 (2007) 1447-1465.
16. W. David, Neural network credit scoring models, Computers and Operations Research 27 (2000) 1131-1152.
17. V.S. Desai, J.N. Crook, G.A. Overstreet, A comparison of neural networks and linear scoring models in the credit union environment, European Journal of Operational Research 95 (1996) 24-37.
18. W.H. Greene, Econometric Analysis, third ed., Prentice-Hall, 1997.
19. Y.P. Gupta, M.C. Gupta, A.K. Kumar, C. Sundram, Minimizing total intercell and intracell moves in cellular manufacturing: a genetic algorithm approach, International Journal of Computer Integrated Manufacturings (1995) 92-101.
20. J. Han, M. Kamber, Data Mining: Concepts and Techniques, second ed., Morgan Kaufman Publishers, San Francisco, CA, 2006.
21. D.J. Hand, W.E. Henley, Statistical classification methods in consumer credit scoring: a review, Journal of the Royal Statistical Society 160 (2007) 523-541.
22. J.V. Hansen, Combining predictors: comparison of five meta machine learning methods, Information Sciences 119 (1999) 91-105.
23. N.-C. Hsieh, An integrated data mining and behavioral scoring model for analyzing bank customers, Expert Systems with Applications 27 (2004) 623-633.
24. N.-C. Hsieh, Hybrid mining approach in the design of credit scoring models, Expert Systems with Applications 28 (2005) 655-665.
25. X. Hu, A data mining approach for retailing bank customer attrition analysis, Applied Intelligence 22 (2005) 47-60.
26. Y.-C. Hu, J. Ansell, Measuring retail company performance using credit scoring techniques, European Journal of Operational Research 183 (2007) 1595-1606.
27. C.L. Huang, M.C. Chen, C.J. Wang, Credit scoring with a data mining approach based on support vector machines, Expert Systems with Applications 33 (2007) 847-856.
28. J.-J. Huang, G.-H. Tzeng, C.-S. Ong, Two-stage genetic programming (2SGP) for the credit scoring model, Applied Mathematics and Computation 174 (2006) 1039-1053.
29. T. lmielinski, H. Mannila, A database perspective on knowledge discovery, Communications of the ACM 40 (1996) 214-225.
30. BA. Jain, B.N. Nag, Performance evaluation of neural network decision models, Journal of Management 14 (1997) 201-215.
31. E. Kim, W. Kim, Y. Lee, Purchase propensity prediction of EC customer by combining multiple classifiers based on GA, in: Proceedings of the International Conference on Electronic Commerce, Seoul, Korea, 2000, pp. 274-280.
32. T. Kohonen, Self-Organizing Maps, third ed., Springer-Verlag, Berlin, Germany, 2001.
33. P.R. Kumar, V. Ravi, Bankruptcy prediction in banks and firms via statistical and intelligent techniques - a review, European Journal of Operational Research 180 (2007) 1-28.
34. A. Laha, Building contextual classifiers by integrating fuzzy rule based classification technique and k-NN method for credit scoring, Advanced Engineering Informatics 21 (2007) 281-291.
35. T.S. Lee, C.C. Chiu, C.J. Lu, I.F. Chen, Credit scoring using the hybrid neural discriminant technique, Expert Systems with Applications 23 (2002) 245-254.
36. T.S. Lee, C.C. Chiu, Y.C. Chou, C.J. Lu, Mining the customer credit using classification and regression tree and multivariate adaptive regression splines, Computational Statistics and Data Analysis 50 (2006) 1113-1130.
37. R. Malhotra, D.K. Malhotra, Evaluating consumer loans using neural networks, Omega 31 (2003) 83-96.
38. D. Maitens, B. Baesens, T. Van Gestel, J. Vanthienen, Comprehensible credit scoring models using rule extraction from support vector machines, European Journal of Operational Research 183 (2007) 1466-1476.
39. G.W. Milligan, M.C. Cooper, An examination of procedures for determining the number of clusters in a data set, Psychometrika 50(1985) 159-179.
40. J.H. Min, Y.-C. Lee, A practical approach to credit scoring, Expert Systems with Applications 35 (2008) 1762-1770.
41. N. Sarlija, M. Bensic, Z. Bohacek, Customer revolving credit - how the economic conditions make a difference, in: Credit Scoring Conference, 2005. 〈http://www.crc.man.ed.ac.uk/conference/archive/2005.html〉.
42. N. Sarlija, M. Bensic, M. Zekic-Susac, A neural network classification of credit applicants in consumer credit scoring, in: Proceedings of the 24th IASTED International Conference on Artificial Intelligence and Applications, Innsbruck, Austria, 2006, pp. 205-210.
43. S.Y. Sohn, H.W. Shin, Reject inference in credit operations based on survival analysis, Expert Systems with Applications 31 (2006) 26-29.
44. L.C. Thomas, A survey of credit and behavioral scoring: forecasting financial risk of lending to consumers, International Journal of Forecasting 16 (2000) 149-172.
45. L.C. Thomas, J. Ho, W.T. Scherer, Time will tell: behavioral scoring and the dynamics of consumer credit assessment, IMA Journal of Management Mathematics 12 (2001) 89-103.
46. R. Tsaih, Y.-J. Liu, W. Liu, Y.-L. Lien, Credit scoring system for small business loans, Decision Support Systems 38 (2004) 91-99.
47. D. West, Neural network credit scoring models, Computers and Operations Research 27 (2000) 1131-1152.
48. D. West, S. Dellana, J. Qian, Neural network ensemble strategies for financial decision applications, Computers and Operations Research 32 (2005) 2543-2559.
49. D. West, S. Dellana, Diversity of ability and cognitive style for group decision processes, Information Sciences 179 (2009) 542-558.
50. E.C.-C. Yen, Warning signals for potential accounting frauds in blue chip companies - an application of adaptive resonance theory, Information Sciences 177 (2007) 4515-4525.
51. Z. Zhu, H. He, J.A. Starzyk, C. Tseng, Self-organizing learning array and its application to economic and financial problems, Information Sciences 177 (2007)1180-1192.