CREDIT SCORING, STATISTICAL TECHNIQUES AND EVALUATION CRITERIA: A REVIEW OF THE LITERATURE

Intelligent Systems in Accounting, Finance and Management

Volumn 18, Issue 2-3, 2011, Pages 59-88

  Abdou, Hussein A ^a   Pointon, John ^b  

^a UNIVERSITY OF SALFORD   (United Kingdom)

^b UNIVERSITY OF PLYMOUTH   (United Kingdom)

Author keywords

classification techniques; credit scoring; literature review; performance evaluation criteria

Indexed keywords

EID: 85104787192     PISSN: 15501949     EISSN: 21600074     Source Type: Journal    
DOI: 10.1002/isaf.325     Document Type: Article

References (214)

1. Abdou H. 2009a. Credit scoring models for Egyptian banks: neural nets and genetic programming versus conventional techniques. PhD thesis, The University of Plymouth, UK.
2. Abdou H. 2009b. An evaluation of alternative scoring models in private banking. Journal of Risk Finance 10(1): 38–53.
3. Abdou H. 2009c. Genetic programming for credit scoring: the case of Egyptian public sector banks. Expert Systems with Applications 36(9): 11402–11417.
4. Abdou H, Pointon J. 2009. Credit scoring and decision-making in Egyptian public sector banks. International Journal of Managerial Finance 5(4): 391–406.
5. Abdou H, Pointon J, El Masry A. 2008. Neural nets versus conventional techniques in credit scoring in Egyptian banking. Expert Systems with Applications 35(3): 1275–1292.
6. Ainscough TL, Aronson JE. 1999. An empirical investigation and comparison of neural networks and regression for scanner data analysis. Journal of Retailing and Consumer Services 6(4): 205–217.
7. Al Amari A. 2002. The credit evaluation process and the role of credit scoring: a case study of Qatar. PhD thesis, University College Dublin.
8. Altman EI. 1968. Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance XXIII(4): 589–609.
9. Altman EI. 2005. An emerging market credit scoring system for corporate bonds. Emerging Markets Review 6(4): 311–323.
10. Altman EI, Haldeman R. 1995. Corporate credit scoring models: approaches and tests for successful implementation. Journal of Commercial Lending 77(9): 10–22.
11. Altman EI, Marco G, Varetto F. 1994. Corporate distress diagnosis: comparisons using linear discriminant analysis and neural networks (the Italian experience). Journal of Banking and Finance 18(3): 505–529.
12. Anderson R. 2007. The Credit Scoring Toolkit: Theory and Practice for Retail Credit Risk Management and Decision Automation. Oxford University Press: New York.
13. Anderson TW. 2003. An Introduction to Multivariate Statistical Analysis. Wiley-Interscience: New York.
14. Andreeva G. 2006. European genetic scoring models using survival analysis. Journal of the Operational Research Society 57(10): 1180–1187.
15. Arminger G, Enache D, Bonne T. 1997. Analyzing credit risk data: a comparison of logistic discriminant, classification tree analysis, and feedforward networks. Computational Statistics 12(2): 293–310.
16. Atiya AF. 2001. Bankruptcy prediction for credit risk using neural networks: a survey and new results. IEEE Transactions on Neural Networks 12(4): 929–935.
17. Baesens B. 2003. Developing intelligent systems for credit scoring using machine learning techniques. PhD thesis no. 180 Faculteit Economische en Toegepaste Economische Wetebnschappen, Katholieke Universiteit, Leuven.
18. Baesens B, Gestel TV, Viaene S, Stepanova M, Suykens J, Vanthienen J. 2003. Benchmarking state-of-the-art classification algorithms for credit scoring. Journal of the Operational Research Society 54(6): 627–635.
19. Baesens B, Gestel TV, Stepanova M, Van den Poel D, Vanthienen J. 2005. Neural network survival analysis for personal loan data. Journal of the Operational Research Society 56(9): 1089–1098.
20. Baestaens D-E. 1999. Credit risk modeling strategies: the road to serfdom? Intelligent Systems in Accounting, Finance and Management 8(4): 225–235.
21. Bailey M. 2001. Credit Scoring: The Principles and Practicalities. White Box Publishing: Bristol.
22. Bailey M. 2004. Consumer Credit Quality: Underwriting, Scoring, Fraud Prevention and Collections. White Box Publishing: Bristol.
23. Banasik J, Crook J. 2005. Credit scoring, augmentation, and lean models. Journal of the Operational Research Society 56(9): 1072–1081.
24. Banasik J, Crook J. 2007. Reject inference, augmentation, and sample selection. European Journal of Operational Research 183(3): 1582–1594.
25. Banasik J, Crook J. 2010 Reject inference in survival analysis by augmentation. Journal of Operational Research Society 61(3): 473–458.
26. Banasik J, Crook J, Thomas L. 2001. Scoring by usage. Journal of the Operational Research Society 52(9): 997–1006.
27. Banasik J, Crook J, Thomas L. 2003. Sample selection bias in credit scoring models. Journal of the Operational Research Society 54(8): 822–832.
28. Behrman M, Linder R, Assadi AH, Stacey BR, Backonja MM. 2007. Classification of patients with pain based on neuropathic pain symptoms: comparison of an artificial neural network against an established scoring system. European Journal of Pain 11(4): 370–376.
29. Bellotti T, Crook J. 2009. Support vector machines for credit scoring and discovery of significant features. Expert Systems with Applications 36(2/2): 3302–3308.
30. Ben-David A, Frank E. 2009. Accuracy of machine learning models versus ‘hand crafted’ expert systems – a credit scoring case study. Expert Systems with Applications 36(3): 5264–5271.
31. Bensic M, Sarlija N, Zekic-Susac M. 2005. Modelling small-business credit scoring by using logistic regression, neural networks and decision trees. Intelligent Systems in Accounting, Finance and Management 13(3): 133–150.
32. Beynon MJ. 2005. Optimizing object classification under ambiguity/ignorance: application to the credit rating problem. Intelligent Systems in Accounting, Finance and Management 13(2): 113–130.
33. Bishop CM. 1995. Neural Networks for Pattern Recognition. Oxford University Press: New York.
34. Blochlinger A, Leippold M. 2006. Economic benefit of powerful credit scoring. Journal of Banking and Finance 30(3): 851–873.
35. Boritz JE, Kennedy DB. 1995. Effectiveness of neural network types for prediction of business failure. Expert Systems with Applications 9(4): 503–512.
36. Boyes WJ, Hoffman DL, Low SA. 1989. An econometric analysis of the bank credit scoring problem. Journal of Econometrics 40(1): 3–14.
37. Boyle M, Crook JN, Hamilton R, Thomas LC. 1992. Methods for credit scoring applied to slow payers. In Credit Scoring and Credit Control, Thomas LC, Crook JN, Edelman DB (eds). Oxford University Press: Oxford; 75–90.
38. Breiman L, Friedman JH, Olshen RA, Stone CJ. 1984. Classification and Regression Trees. Wadsworth: Belmont, CA.
39. Cameron AC, Trivedi PK. 1996. Count data models for financial data. In Statistical Methods in Finance, Maddala GS, Rao CR (eds), Handbook of Statistics, vol. 14. North-Holland: Amsterdam; 363–391.
40. Caouette JB., Altman EI., Narayanan P. 1998. Managing Credit Risk: The Next Great Financial Challenge. John Wiley and Sons Inc.: New York.
41. Capon N. 1982. Credit scoring systems: a critical analysis. Journal of Marketing 46(2): 82–91.
42. Carter DA, McNulty JE. 2005. Deregulation, technology change, and the business-lending performance of large and small banks. Journal of Banking and Finance 29(5): 1113–1130.
43. Casu B, Girardone C, Molyneux P. 2006. Introduction to Banking. Prentice Hall: London.
44. Chandler GG, Coffman JY. 1979. A comparative analysis of empirical vs. judgemental credit evaluation. The Journal of Retail Banking 1(2): 15–26.
45. Chen M, Huang S. 2003. Credit scoring and rejected instances reassigning through evolutionary computation techniques. Expert Systems with Applications 24(4): 433–441.
46. Chen Y, Guo R-J, Huang R-L. 2009. Two stages credit evaluation in bank loan appraisal. Economic Modelling 26(1): 63–70.
47. Chiang WK, Zhang D, Zhou L. 2006. Predicting and explaining patronage behavior toward web and traditional stores using neural networks: a comparative analysis with logistic regression. Decision Support Systems 41(2): 514–531.
48. Chuang C, Lin R. 2009. Constructing a reassigning credit scoring model. Expert Systems with Applications 36(2/1): 1685–1694.
49. Coffman JY. 1986. The proper role of tree analysis in forecasting the risk behaviour of borrowers. MDS Reports 3, 4, 7 and 9. Management Decision Systems, Atlanta.
50. Cramer JS. 2004. Scoring bank loans that may go wrong: a case study. Statistica Neerlandica 58(3): 365–380.
51. Crook JN. 1996. Credit scoring: an overview. Working paper series No. 96/13, British Association, Festival of Science, University of Birmingham, The University of Edinburgh.
52. Crook J, Edelman D, Thomas L. 2007. Recent developments in consumer credit risk assessment. European Journal of Operational Research 183(3): 1447–1465.
53. Dasgupta CG, Dispensa GS, Ghose S. 1994. Comparing the predictive performance of a neural network model with some traditional market response models. International Journal of Forecasting 10(2): 235–244.
54. Delen D, Walker G, Kadam A. 2005. Predicting breast cancer survivability: a comparison of three data mining methods. Artificial Intelligence in Medicine 34(2): 113–127.
55. Desai VS, Crook JN, Overstreet GA. 1996. A comparison of neural networks and linear scoring models in the credit union environment. European Journal of Operational Research 95(1): 24–37.
56. Desai VS, Conway DG, Crook JN, Overstreet GA. 1997. Credit scoring models in the credit union environment using neural networks and genetic algorithms. IMA Journal of Mathematics Applied in Business and Industry 8(4): 323–3463.
57. TM linear genetic programming software with support vector machines, classification trees, neural networks and human experts, White Paper. http://www.rmltech.com/ (accessed 10 June 2008).
58. DeYoung R, Frame WS, Glennon D, McMillen DP, Nigro P. 2008. Commercial lending distance and historically underserved area. Journal of Economics and Business 60(1–2): 149–164.
59. Dimla DE, Lister PM. 2000. On-line metal cutting tool condition monitoring. II: Tool-state classification using multi-layer perceptron neural networks. International Journal of Machine Tools and Manufacture 40(5): 769–781.
60. Duliba K. 1991. Contrasting neural nets with regression in predicting performance. In Proceedings of the Twenty-Fourth Annual Hawaii International Conference on System Sciences, vol. 4, IEEE Press: Alamitos, CA; 163–170.
61. Durand D. 1941. Risk Elements in Consumer Instalment Financing, Studies in Consumer Instalment Financing. National Bureau of Economic Research: New York.
62. Dutta S, Shekhar S, Wong WY. 1994. Decision support in non-conservative domains: generalization with neural networks. Decision Support Systems 11(5): 527–544.
63. Dvir D, Ben-David A, Sadeh A, Shenhar AJ. 2006. Critical managerial factors affecting defense projects success: a comparison between neural network and regression analysis. Engineering Applications of Artificial Intelligence 19(5): 535–543.
64. Eisenbeis RA. 1977. Pitfalls in the application of discriminant analysis in business, finance, and economics. Journal of Banking and Finance 32(3): 875–900.
65. Eisenbeis RA. 1978. Problems in applying discriminant analysis in credit scoring models. Journal of Banking and Finance 2(3): 205–219.
66. Elliott, R, Filinkov, A. 2008. A self tuning model for risk estimation. Expert Systems with Applications 34(3): 1692–1697.
67. Emel A, Oral M, Reisman A, Yolalan R. 2003. A credit scoring approach for the commercial banking sector. Socio-Economic Planning Sciences 37(2): 103–123.
68. Etemadi H, Rostamy A, Dehkordi H. 2009. A genetic programming model for bankruptcy prediction: empirical evidence from Iran. Expert Systems with Applications 36(2/2): 3199–3207.
69. Falbo P. 1991. Credit scoring by enlarged discriminant model. Omega – The International Journal of Management Science 19(4): 35–54.
70. Feelders AJ. 2000. Credit scoring and reject inference with mixture models. Intelligent Systems in Accounting, Finance and Management 9(1): 1–8.
71. Feng C-X, Wang X. 2002. Digitizing uncertainty modeling for reverse engineering applications: Regression versus neural networks. Journal of Intelligent Manufacturing 13(3): 189–199.
72. Finney PJ. 1952. Probit Analysis. Cambridge University Press: Cambridge.
73. Fisher RA. 1936. The use of multiple measurements in taxonomic problems. Annals of Eugenics 7(2): 179–188.
74. Fletcher D, Goss E. 1993. Forecasting with neural networks: an application using bankruptcy data. Information and Management 24(3): 159–167.
75. Foglia A, Laviola S, Reedtz P. 1998. Multiple banking relationships and the fragility of corporate borrowers. Journal of Banking and Finance 22(10–11): 1441–1456.
76. Frame W, Srinivasan A, Woosley L. 2001. The effect of credit scoring on small-business lending. Journal of Money, Credit and Banking 33(3): 815–825.
77. Frame W, Padhi M, Woosley L. 2004. Credit scoring and the availability of small business credit in low-and moderate-income areas. The Financial Review 39(1): 35–54.
78. Freund RJ, William WJ. 1998. Regression Analysis: Statistical Modeling of a Response Variable. Academic Press: San Diego, CA.
79. Fritz S, Hosemann D. 2000. Restructuring the credit process: behaviour scoring for German corporates. Intelligent Systems in Accounting, Finance and Management 9(1): 9–21.
80. Gately E. 1996. Neural Networks for Financial Forecasting: Top Techniques for Designing and Applying the Latest Trading Systems. John Wiley and Sons, Inc.: New York.
81. Glen J. 2001. Classification accuracy in discriminant analysis: a mixed integer programming approach. Journal of the Operational Research Society 52(3): 328–339.
82. Goldberg DE. 1989. Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley: Boston, MA.
83. Grablowsky BJ, Talley WK. 1981. Probit and discriminant functions for classifying credit applicants: a comparison. Journal of Economic and Business 33(3): 254–261.
84. Greene W. 1998. Sample selection in credit scoring models. Japan and the World Economy 10(3): 299–316.
85. Guillen M, Artis M. 1992. Count data models for a credit scoring system. In The European Conference Series in Quantitative Economics and Econometrics on Econometrics of Duration, Count and Transition Models, Paris.
86. Gup BE, Kolari JW. 2005. Commercial Banking: The Management of Risk. John Wiley and Sons, Inc.: Alabama.
87. Hand DJ. 1981. Discrimination and Classification. John Wiley and Sons, Inc.: New York.
88. Hand DJ. 1997. Construction and Assessment of Classification Rules. John Wiley and Sons, Ltd: Chichester.
89. Hand DJ, Henley WE. 1997. Statistical classification methods in consumer credit scoring: a review. Journal of the Royal Statistical Society: Series A (Statistics in Society) 160(3): 523–541.
90. Hand DJ, Jacka SD. 1998. Statistics in Finance. Arnold: London.
91. Hand DJ, Oliver JJ, Lunn AD. 1998. Discriminant analysis when the classes arise from a continuum. Pattern Recognition 31(5): 641–650.
92. Hand DJ, Sohn SY, Kim Y. 2005. Optimal bipartite scorecards. Expert Systems with Applications 29(3): 684–690.
93. Hardgrave BC, Wilson RL, Walstrom KA. 1994. Predicting graduate student success: a comparison of neural networks and traditional techniques. Computers and Operations Research 29(30): 249–263.
94. Haughwout A, Peach R, Tracy J. 2008. Juvenile delinquent mortgages: bad credit or bad economy? Journal of Urban Economics 64(2): 246–257.
95. Haykin S. 1994. Neural Networks: A Comprehensive Foundation. Prentice Hall: London.
96. Heffernan S. 2005. Modern Banking. John Wiley and Sons, Ltd: Chichester.
97. Heiat A. 2002. Comparison of artificial neural network and regression models for estimating software development effort. Information and Software Technology 44(15): 911–922.
98. Henley WE. 1995. Statistical aspects of credit scoring. PhD thesis, The Open University, Milton Keynes.
99. Henley WE, Hand DJ. 1996. A k-nearest-neighbour classifier for assessing consumer credit risk. The Statistician 45(1): 77–95.
100. Heuson A, Passmore W, Sparks R. 2001. Credit scoring and mortgage securitization: implications for mortgage rates and credit availability. Journal of Real Estate Finance and Economics 23(3): 337–363.
101. Hill T, Remus W. 1994. Neural network models for intelligent support of managerial decision making. Decision Support Systems 11(5): 449–459.
102. Hoffmann F, Baesens B, Mues C, Gestel TV, Vanthienen J. 2007. Inferring descriptive and approximate fuzzy rules for credit scoring using evolutionary algorithms. European Journal of Operational Research 177(1): 540–555.
103. Hosmer DW, Lemeshow S. 1989. Applied Logistic Regression. John Wiley and Sons, Inc.: New York.
104. Hsieh N-C. 2004. An integrated data mining and behavioral scoring model for analysing bank customers. Expert Systems with Applications 27(4): 623–633.
105. Hu Y. 2008. Incorporating a non-additive decision making method into multi-layer neural networks and its application to financial distress analysis. Knowledge-Based Systems 21(5): 383–390.
106. Hu Y-C, Ansell J. 2007. Measuring retail company performance using credit scoring techniques. European Journal of Operational Research 183(3): 1595–1606.
107. Huang J, Tzeng G, Ong C. 2006. Two-stage genetic programming (2SGP) for the credit scoring model. Applied Mathematics and Computation 174(2): 1039–1053.
108. Huang C, Chen M, Wang C. 2007. Credit scoring with a data mining approach based on support vector machines. Expert Systems with Applications 33(4): 847–856.
109. Ignizio JP, Soltys JR. 1996. An ontogenic neural network for bankruptcy classification. IMA Journal of Mathematics Applied in Business and Industry 7(4): 313–325.
110. Irwin GW, Warwick K, Hunt KJ. 1995. Neural Networks Applications in Control. The Institution of Electronic Engineers: London.
111. Jo H, Han I, Lee H. 1997. Bankruptcy prediction using case-based reasoning, neural network and discriminant analysis. Expert Systems with Applications 13(2): 97–108.
112. Kay JW, Titterington DM. 1999. Statistics and Neural Networks: Advanced at the Interface. Oxford University Press: New York.
113. Kim YS, Sohn SY. 2004. Managing loan customers using misclassification patterns of credit scoring model. Expert Systems with Applications 26(4): 567–573.
114. Koza JR. 1992. Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press: Cambridge, MA.
115. Koza JR. 1994. Genetic Programming II: Automation Discovery of Reusable Programs. MIT Press: Cambridge, MA.
116. Krishnaswamy M, Krishnan P. 2002. Nozzle wear rate prediction using regression and neural network. Biosystems Engineering 82(1): 53–64.
117. Kumar A, Motwani J. 1999. Reengineering the lending procedure for small businesses: a case study. Work Study 48(1): 6–12.
118. Kumar A, Rao VR, Soni H. 1995. An empirical comparison of neural network and logistic regression models. Marketing Letters 6(4): 251–263.
119. Kumra R, Stein R, Assersohn I. 2006. Assessing a knowledge-based approach to commercial loan underwriting. Expert Systems with Applications 30(3): 507–518.
120. Laha A. 2007. Building contextual classifiers by integrating fuzzy rule based classification technique and k-nn method for credit scoring. Advanced Engineering Informatics 21(3): 281–291.
121. Landajo M, Andres JD, Lorca P. 2007. Robust neural modelling for the cross-sectional analysis of accounting information. European Journal of Operational Research 177(2): 1232–1252.
122. Lee K, Booth D, Alam P. 2005. A comparison of supervised and unsupervised neural networks in predicting bankruptcy of Korean firms. Expert Systems with Applications 29(1): 1–16.
123. Lee T, Chen I. 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.
124. Lee TH, Jung S. 2000. Forecasting creditworthiness: logistic vs. artificial neural net. The Journal of Business Forecasting Methods and Systems 18(4): 28–30.
125. Lee T, Chiu C, Lu C, Chen I. 2002. Credit scoring using the hybrid neural discriminant technique. Expert Systems with Applications 23(3): 245–254.
126. Lenard MJ, Alam P, Madey GR. 1995. The application of neural networks and a qualitative response model to the auditor's going concern uncertainty decision. Decision Sciences 26(2): 209–227.
127. Lensberg T, Eilifsen A, McKee T. 2006. Bankruptcy theory development and classification via genetic programming. European Journal of Operational Research 169(2): 677–697.
128. Leonard KJ. 1992. Credit scoring models for the evaluation of small-business loan applications. IMA Journal of Mathematics Applied in Business and Industry 4(1): 89–95.
129. Leonard KJ. 1993. Detecting credit card fraud using expert systems. Computers and Industrial Engineering 25(1–4): 103–106.
130. Leonard KJ. 1995. The development of credit scoring quality measures for consumer credit application. International Journal of Quality and Reliability Management 12(4): 79–85.
131. Leonard KJ. 1996. Information systems and benchmarking in the credit scoring industry. Benchmarking for Quality Management and Technology 3(1): 38–44.
132. Leshno M, Spector Y. 1996. Neural network prediction analysis: the bankruptcy case. Neurocomputing 10(2): 125–147.
133. Lewis EM. 1992. An Introduction to Credit Scoring. Fair, Isaac and Co., Inc.: San Rafael, CA.
134. Liang Q. 2003. Corporate financial distress diagnosis in China: empirical analysis using credit scoring models. Hitotsubashi Journal of Commerce and Management 38(1): 13–28.
135. Lim MK, Sohn SY. 2007. Cluster-based dynamic scoring model. Expert Systems with Applications 32(2): 427–431.
136. Limsombunchai V, Gan C, Lee M. 2005. An analysis of credit scoring for agricultural loans in Thailand. American Journal of Applied Sciences 2(8): 1198–1205.
137. Long MS. 1973. Credit scoring development for optimal credit extension and management control. Unpublished PhD Thesis, Purdue University.
138. Lovie AD. 1987. The bootstrapped – lessons for the acceptance of intellectual technology. Applied Ergonomics 18(3): 201–206.
139. Lucas A. 1992. Updating scorecards: removing the mystique. In Credit Scoring and Credit Control, Thomas LC, Crook JN, Edelman DB (eds). Oxford University Press: Oxford; 180–197.
140. Maddala GS. 2001. Introduction to Econometrics. John Wiley and Sons, Ltd: Chichester.
141. Malhotra R, Malhotra DK. 2003. Evaluating consumer loans using neural networks Omega – The International Journal of Management Science 31(2): 83–96.
142. Masters T. 1995. Advanced Algorithms for Neural Networks: A C++ Sourcebook. John Wiley and Sons, Inc.: New York.
143. Mays E. 2001. Handbook of Credit Scoring. Glenlake Publishing Company, Ltd: Chicago, IL.
144. Mays E. 2004. The rule of credit scores in consumer lending. In Credit Scoring for Risk Managers: The Handbook for Lenders, Mays E (ed.). Thomson South-Western: Australia; 3–12.
145. McKee T, Lensberg T. 2002. Genetic programming and rough sets: a hybrid approach to bankruptcy classification. European Journal of Operational Research 138(2): 436–451.
146. Min JH, Jeong C. 2009. A binary classification method for bankruptcy prediction. Expert Systems with Applications 36(3): 5256–5263.
147. Min JH, Lee Y-C. 2008. A practical approach to credit scoring. Expert Systems with Applications 35(4): 1762–1770.
148. Mukkamala S, Vieira A, Sung A. 2008. Model selection and feature ranking for financial distress classification. http://www.rmltech.com/ (accessed 10 June 2008).
149. Myers JH, Forgy EW. 1963. The development of numerical credit evaluation systems. Journal of the American Statistical Association 58(303): 799–806.
150. Nakamura E. 2005. Inflation forecasting using a neural network. Economics Letters 86(3): 373–378.
151. Nanni L, Lumini A. 2009. An experimental comparison of ensemble of classifiers for bankruptcy prediction and credit scoring. Expert Systems with Applications 36(2/2): 3028–3033.
152. Nath R, Rajagopalan B, Ryker R. 1997. Determining the saliency of input variables in neural network classifiers. Computers and Operations Researches 24(8): 767–773.
153. Nelson MM, Illingworth WT. 1990. A Practical Guide to Neural Nets. Addison Wesley: New York.
154. Nguyen T, Malley R, Inkelis SH, Kuppermann N. 2002. Comparison of prediction models for adverse outcome in pediatric meningococcal disease using artificial neural network and logistic regression analyses. Journal of Clinical Epidemiology 55(7): 687–695.
155. Nikolopoulos K, Goodwin P, Patelis A, Assimakopoulos V. 2007. Forecasting with cue information: a comparison of multiple regression with alternative forecasting approaches. European Journal of Operational Research 180(1): 354–368.
156. Núñez-Letamendia L. 2002. Trading systems designed by genetic algorithms. Managerial Finance 28(8): 87–106.
157. Ong C, Huang J, Tzeng G. 2005. Building credit scoring models using genetic programming. Expert Systems with Applications 29(1): 41–47.
158. Orgler YE. 1970. A credit scoring model for commercial loans. Journal of Money, Credit and Banking II(4): 435–445.
159. Orgler YE. 1971. Evaluation of bank consumer loans with credit scoring models. Journal of Bank Research 2(1): 31–37.
160. Ottenbacher KJ, Smith PM, Illig SB, Linn RT, Mancuso M, Granger CV. 2004. Comparison of logistic regression and neural network analysis applied to predicting living setting after hip fracture. Annals of Epidemiology 14(8): 551–559.
161. Paleologo G, Elisseeff A, Antonini G. 2010 Subagging for credit scoring models. European Journal of Operational Research 201(2): 490–499.
162. Palisade Corporation. 2005. Neural tools: neural networks add-in for Microsoft Excel. Version 1.0. Palisade Corporation, New York.
163. Paliwal M, Kumar UA. 2009. Neural networks and statistical techniques: a review of applications. Expert Systems with Applications 36(1): 2–17.
164. Pendharkar PC. 2005. A threshold-varying artificial neural network approach for classification and its application to bankruptcy prediction problem. Computers and Operations Research 32(10): 2561–2582.
165. Pindyck RS, Rubinfeld DL. 1997. Econometric Models and Economic Forecasts. McGraw-Hill/Irwin.
166. Quah JTS, Sriganesh M. 2008. Real-time credit card fraud detection using computational intelligence. Expert Systems with Applications 35(4): 1721–1732.
167. Raiffa H, Schlaifer R. 1961. Applied Statistical Decision Theory. Harvard University Press: Boston, MA.
168. Reed RD, Marks RJ. 1999. Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks. The MIT Press: London.
169. Rosenberg E, Gleit A. 1994. Quantitative methods in credit management: a survey. Operations Research 42(4): 589–613.
170. Salchenberger LM, Cinar EM, Lash NA. 1992. Neural networks: a new tool for predicting thrift failures. Decision Sciences 23(4): 899–916.
171. Sarlija N, Bensic M, Bohacek Z. 2004. Multinomial model in consumer credit scoring. In 10th International Conference on Operational Research, Trogir, Croatia.
172. Sarlija N, Bensic M, Zekic-Susac M. 2009. Comparison procedure of predicting the time to default in behavioural scoring. Expert Systems with Applications 36(5): 8778–8788.
173. Seow H, Thomas LC. 2006. Using adaptive learning in credit scoring to estimate take-up probability distribution. European Journal of Operational Research 173(3): 880–892.
174. Shang JS, Lin YE, Goetz AM. 2000. Diagnosis of MRSA with neural networks and logistic regression approach. Health Care Management Science 3(4): 287–297.
175. Siddiqi N. 2006. Credit Risk Scorecards: Developing and Implementing Intelligent Credit Scoring. John Wiley and Sons, Inc.: Hoboken, NJ.
176. Sinha AP, Richardson Ma. 1996. A Case-Based Reasoning System for Indirect Bank Lending. Intelligent Systems in Accounting, Finance and Management 5(4): 229–240.
177. Smith AE, Mason AK. 1997. Cost estimation predictive modeling: regression versus neural network. The Engineering Economist 42(2): 137–161.
178. Somers M, Whittaker J. 2007. Quantile regression for modelling distributions of profit and loss. European Journal of Operational Research 183(3): 1477–1487.
179. Song JH, Venkatesh SS, Conant EA, Arger PH, Sehgal SM. 2005. Comparative analysis of logistic regression and artificial neural network for computer-aided diagnosis of breast masses. Academic Radiology 12(4): 487–495.
180. Spear NA, Leis M. 1997. Artificial neural networks and the accounting method choice in the oil and gas industry. Accounting Management and Information Technology 7(3): 169–181.
181. Steenackers A, Goovaerts M. J. 1989. A credit scoring model for personal loans. Insurance: Mathematics and Economics 8(8): 31–34.
182. Stefanowski, J, Wilk, S. 2001. Evaluating business credit risk by means of approach-integrating decision rules and case-based learning. Intelligent Systems in Accounting, Finance and Management 10(2): 97–114.
183. Sullivan AC. 1981. Consumer finance. In Financial Handbook, Altman EI (ed.). John Wiley and Sons, Inc.: New York; 9.3–9.27.
184. Sustersic M, Mramor D, Zupan J. 2009. Consumer credit scoring models with limited data. Expert Systems with Applications 36(3): 4736–4744.
185. Tam KY, Kiang MY. 1992. Managerial applications of neural networks: the case of bank failure predictions. Management Science 38(7): 926–947.
186. Teller A, Veloso M. 2000. Internal reinforcement in a connectionist genetic programming approach. Artificial Intelligence 120(2): 165–198.
187. Thanh Dinh T-H, Kleimeier S. 2007. A credit scoring model for Vietnam's retail banking market. International Review of Financial Analysis 16(5): 471–495.
188. Thieme RJ, Song M, Calantone RJ. 2000. Artificial neural network decision support systems for new product development project selection. Journal of Marketing Research 37(4): 499–507.
189. Thomas LC. 1998. Methodologies for classifying applicants for credit. In Statistics in Finance, Hand DJ, Jacka SD (eds). Arnold: London; 83–103.
190. Thomas LC. 2000. A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers. International Journal of Forecasting 16(2): 149–172.
191. Thomas LC, Edelman DB, Crook LN. 2002. Credit Scoring and Its Applications. Society for Industrial and Applied Mathematics: Philadelphia, PA.
192. Thomas LC, Edelman DB, Crook JN. 2004. Readings in Credit Scoring: Recent Developments, Advances, and Aims. Oxford University Press: New York.
193. Thompson P. 1998. Bank lending and the environment: policies and opportunities. International Journal of Bank Marketing 16(6): 243–252.
194. Trinkle BS, Baldwin AA. 2007. Interpretable credit model development via artificial neural networks. Intelligent Systems in Accounting, Finance and Management 15(3–4): 123–147.
195. Trippi RR, Turban E. 1993. Neural Networks in Finance and Investing: Using Artificial Intelligence to Improve Real-World Performance. Irwin: Chicago, IL.
196. Tsai C, Wu J. 2008. Using neural networks ensembles for bankruptcy prediction and credit scoring. Expert Systems with Applications 34(4): 2639–2649.
197. Tsaih R, Liu Y, Liu W, Lien Y. 2004. Credit scoring system for small business loans. Decision Support Systems 38(1): 91–99.
198. Usha AK. 2005. Comparison of neural networks and regression analysis: a new insight. Expert Systems with Applications 29(2): 424–430.
199. Van Gestel T, Baesens B, Suykens JAK, Van den Poel D, Baestaens D-E, Willekens M. 2006. Bayesian kernel based classification for financial distress detection. European Journal of Operational Research 172(3): 979–1003.
200. Verstraeten G, Van den Poel D. 2005. The impact of sample bias on consumer credit scoring performance and profitability. Journal of the Operational Research Society 56(8): 981–992.
201. Walczak S, Sincich T. 1999. A comparative analysis of regression and neural networks for university admissions. Information Sciences 119(1–2): 1–20.
202. Warner B, Misra M. 1996. Understanding neural networks as statistical tools. The American Statistician 50(4): 284–293.
203. West D. 2000. Neural network credit scoring models. Computers and Operations Research 27(11–12): 1131–1152.
204. West D, Dellana S, Qian J. 2005. Neural network ensemble strategies for financial decision applications. Computers and Operations Research 32(10): 2543–2559.
205. Xia Y, Liu B, Wang S, Lai KK. 2000. A model for portfolio selection with order of expected returns. Computers and Operations Research 27(5): 409–422.
206. Yang Y. 2007. Adaptive credit scoring with kernel learning methods. European Journal of Operational Research 183(3): 1521–1536.
207. Yang Z, Wang Y, Bai Y, Zhang X. 2004. Measuring scorecard performance. In Cross-Validation and Ensemble Analyses on Multiple-Criteria Linear Programming Classification for Credit Cardholder Behavior, Bubak M, van Albada G, Sloot P, Dongarra J (eds). Computational Science – ICCS 2004, Lecture Notes in Computer Science vol. 3039. Springer: Berlin; 900–906.
208. Yesilnacar E, Topal T. 2005. Landslide susceptibility mapping: a comparison of logistic regression and neural networks methods in a medium scale study, Hendek region (Turkey). Engineering Geology 79(3–4): 251–266.
209. Yim J, Mitchell H. 2005. Comparison of country risk models: hybrid neural networks, logit models, discriminant analysis and cluster techniques. Expert Systems with Applications 28(1): 137–148.
210. Yoon Y, Swales G Jr, Margavio TM. 1993. A comparison of discriminant analysis versus artificial neural networks. The Journal of the Operational Research Society 44(1): 51–60.
211. Yu L, Wang S, Lai K. 2009. An intelligent-agent-based fuzzy group decision making model for financial multicriteria decision support: the case of credit scoring. European Journal of Operational Research 195(3): 942–959.
212. Zekic-Susac M, Sarlija N, Bensic M. 2004. Small business credit scoring: a comparison of logistic regression, neural networks, and decision tree models. In 26th International Conference on Information Technology Interfaces, Croatia.
213. Zhang G, Hu MY, Patuwo BE, Indro DC. 1999. Artificial neural networks in bankruptcy prediction: general framework and cross-validation analysis. European Journal of Operational Research 116(1): 16–32.
214. Zhang Y, Bhattacharyya S. 2004. Genetic programming in classifying large-scale data: an ensemble method. Information Sciences 163(1–3): 85–101.