An empirical comparison of conventional techniques, neural networks and the three stage hybrid Adaptive Neuro Fuzzy Inference System (ANFIS) model for credit scoring analysis: The case of Turkish credit card data

European Journal of Operational Research - Tập 222 Số 1 - Trang 168-178 - 2012
Soner Akkoç1
1Department of Banking and Finance, School of Applied Sciences, Dumlupınar University, Central Campus, 43100 Kütahya, Turkey

Tóm tắt

Từ khóa


Tài liệu tham khảo

Abdou, 2009, Genetic programming for credit scoring: the case of Egyptian public sector banks, Expert Systems with Applications, 36, 11402, 10.1016/j.eswa.2009.01.076

Abdou, 2008, Neural nets versus conventional techniques in credit scoring in Egyptian banking, Expert Systems with Applications, 35, 1275, 10.1016/j.eswa.2007.08.030

Abonyi, 2003

Akkoç, S., 2007. Bankruptcy Prediction Using Neurofuzzy Modeling and an Empirical Analysis. Doctoral Dissertation. The Dumlupınar University, Kütahya, Turkey.

Alam, 2000, The use of fuzzy clustering algorithm and self-organizing neural network for identifying potentially failing banks: an experiment study, Expert Systems with Applications, 18, 185, 10.1016/S0957-4174(99)00061-5

Altman, 1968, Financial ratios, discriminant analysis and the prediction of corporate bankruptcy, Journal of Finance, 23, 589, 10.1111/j.1540-6261.1968.tb00843.x

Angelini, 2008, A neural network approach for credit risk evaluation, Quarterly Review of Economics and Finance, 48, 733, 10.1016/j.qref.2007.04.001

Aziz, 1988, Bankruptcy prediction – an investigation at cash flow based models, Journal of Management, 25, 419

Banking Regulation and Supervision Agency, 2009. Financial Market Report, Issue:16 Ankara. <http://www.bddk.org.tr/WebSitesi/english/Reports/Financial_Markets_Report/8127FMR_Dec_2009.pdf>.

Basel Committee on Banking Supervision (BCBS), 2005. Studies on the Validation of Internal Rating Systems. Working Paper 14, Basel.

Bell, 1997, Neural nets or the logit model: a comparison of each model’s ability to predict commercial bank failures, Intelligent Systems in Accounting, Finance and Management, 6, 249, 10.1002/(SICI)1099-1174(199709)6:3<249::AID-ISAF125>3.0.CO;2-H

Bellotti, 2009, Support vector machines for credit scoring and discovery of significant features, Expert Systems with Applications, 36, 3302, 10.1016/j.eswa.2008.01.005

Chen, 2009, Alternative diagnosis of corporate bankruptcy: a neuro fuzzy approach, Expert Systems with Applications, 36, 7710, 10.1016/j.eswa.2008.09.023

Chen, 2003, Credit scoring and rejected instances reassigning through evolutionary computation techniques, Expert Systems with Applications, 24, 433, 10.1016/S0957-4174(02)00191-4

Chuang, 2009, Constructing a reassigning credit scoring model, Expert Systems with Applications, 36, 1685, 10.1016/j.eswa.2007.11.067

Cinko, 2006, Comparison of credit scoring techniques, Istanbul Commerce University Social Science Journal, 9, 143

Crook, 2007, Recent developments in consumer credit risk assessment, European Journal of Operational Research, 183, 1447, 10.1016/j.ejor.2006.09.100

Cybenko, 1989, Approximation by superpositions of a sigmoidal function, Mathematical Control Signals Systems, 2, 303, 10.1007/BF02551274

Davalos, 1999, The application of a neural network approach to predicting bankruptcy risks facing the major US air carriers: 1979–1996, Journal of Air Transport Managements, 5, 81, 10.1016/S0969-6997(98)00042-8

Deakin, 1972, A dicriminant analysis of predictors of business failure, Journal of Accounting Research, 10, 167, 10.2307/2490225

Desai, 1996, A comparison of neural networks and linear scoring models in the credit union environment, European Journal of Operational Research, 95, 24, 10.1016/0377-2217(95)00246-4

Durand, 1941

Engelmann, 2003, Testing rating accuracy, Risk, January, 82

Finlay, 2011, Multiple classifier architectures and their application to credit risk assessment, European Journal of Operational Research, 210, 368, 10.1016/j.ejor.2010.09.029

Fisher, 1936, The use of multiple measurements in taxonomic problems, Annual Eugenics, 7, 179, 10.1111/j.1469-1809.1936.tb02137.x

Foreman, 2003, A logistic analysis of bankruptcy within the US local telecommunications industry, Journal of Economics & Business, 55, 135, 10.1016/S0148-6195(02)00133-9

Gentry, 1985, Classifying bankrupt firms with fund flow components, Journal of Accounting Research, 23, 146, 10.2307/2490911

Hand, 1981

Harrell, 1985, A comparison of the discrimination of discriminant analysis and logistic regression

Haykin, 1994

Hecht-Nielsen, 1990

Henley, W.E., 1995. Statistical Aspects of Credit Scoring. Doctoral Dissertation. The Open University, Milton Keynes, UK.

Hornik, 1989, Multilayer feedforward networks are universal approximators, Neural Networks, 2, 359, 10.1016/0893-6080(89)90020-8

Hsieh, 2005, Hybrid mining approach in the design of credit scoring models, Expert Systems with Applications, 28, 655, 10.1016/j.eswa.2004.12.022

Hsieh, 2010, A data driven ensemble classifier for credit scoring analysis, Expert Systems with Applications, 37, 534, 10.1016/j.eswa.2009.05.059

Hsieh, 2004, An integrated data mining and behavioral scoring model for analyzing bank customers, Expert Systems with Applications, 27, 623, 10.1016/j.eswa.2004.06.007

Huang, 2007, Credit scoring with a data mining approach based on support vector machines, Expert Systems with Applications, 33, 847, 10.1016/j.eswa.2006.07.007

Huang, 2006, Two-stage genetic programming (2SGP) for the credit scoring model, Applied Mathematics and Computation, 174, 1039, 10.1016/j.amc.2005.05.027

Jang, 1993, ANFIS: adaptive-network-based fuzzy inference system, IEEE Transactions on Systems, Man, and Cybernettics, 23, 665, 10.1109/21.256541

Jang, 1997

Jensen, 1992, Using neural networks for credit scoring, Managerial Finance, 18, 15, 10.1108/eb013696

Jo, 1997, Bankruptcy prediction using case-based reasoning, neural networks, and discriminant analysis, Expert Systems with Applications, 13, 97, 10.1016/S0957-4174(97)00011-0

Keasey, 1987, Non-financial symptoms and the prediction of small company failure: a test of Argenti’s Hypotheses, Journal of Business Finance & Accounting, 14, 335, 10.1111/j.1468-5957.1987.tb00099.x

Kim, 2010, Support vector machines for default prediction of SMEs based on technology credit, European Journal of Operational Research, 201, 838, 10.1016/j.ejor.2009.03.036

Laitinen, 1999, Predicting a corporate credit analyst’s risk estimate by logistic and linear models, International Review of Financial Analysis, 8, 97, 10.1016/S1057-5219(99)00012-5

Laitinen, 2000, Bankruptcy prediction: application of the Taylor’s expansion in logistic regression, International Review of Financial Analysis, 9, 327, 10.1016/S1057-5219(00)00039-9

Lancher, 1995, A neural network for classifying the financial health of a firm, European Journal of Operational Research, 85, 53, 10.1016/0377-2217(93)E0274-2

Lee, 2005, A two-stage hybrid credit scoring model using artificial neural networks and multivariate adaptive regression splines, Expert Systems with Applications, 28, 743, 10.1016/j.eswa.2004.12.031

Lee, 2002, Credit scoring using the hybrid neural discriminant technique, Expert Systems with Applications, 23, 245, 10.1016/S0957-4174(02)00044-1

Lee, 2006, Mining the customer credit using classification and regression tree and multivariate adaptive regression splines, Computational Statistics and Data Analysis, 50, 1113, 10.1016/j.csda.2004.11.006

Lee, 2007, Application of support vector machines to corporate credit rating prediction, Expert Systems with Applications, 33, 67, 10.1016/j.eswa.2006.04.018

Leshno, 1996, Neural network prediction analysis: the bankruptcy case, Neurocomputing, 10, 125, 10.1016/0925-2312(94)00060-3

Li, 2006, The evaluation of consumer loans using support vector machines, Expert Systems with Applications, 30, 772, 10.1016/j.eswa.2005.07.041

Luo, 2009, Prediction model building with clustering-launched classification and support vector machines in credit scoring, Expert Systems with Applications, 36, 7562, 10.1016/j.eswa.2008.09.028

Malhotra, 2002, Differentiating between good credits and bad credits using neuro-fuzzy systems, European Journal of Operational Research, 136, 190, 10.1016/S0377-2217(01)00052-2

Malhotra, 2003, Evaluating consumer loans using neural networks, Omega, 31, 83, 10.1016/S0305-0483(03)00016-1

Martin, 1977, Early warning of bank failure, Journal of Banking and Finance, 1, 249, 10.1016/0378-4266(77)90022-X

Meyer, 1970, Prediction of bank failures, Journal of Finance, 25, 853, 10.1111/j.1540-6261.1970.tb00558.x

Nanni, 2009, An experimental comparison of ensemble of classifiers for bankruptcy prediction and credit scoring, Expert Systems with Applications, 36, 3028, 10.1016/j.eswa.2008.01.018

Odeh, 2010, Predicting credit default: comparative results from an artificial neural network, logistic regression and adaptive neuro-fuzzy inference system, International Research Journal of Finance and Economics, 42, 7

Odom, M.D., Sharda, R., 1990. A neural network model for bankruptcy prediction. In: Proceedings of the International Joint Conference on Neural Networks, II, pp. 163–167.

Ohlson, 1980, Financial ratios and probabilistic prediction of bankruptcy, Journal of Accounting Research, 18, 109, 10.2307/2490395

Ong, 2005, Building credit scoring models using genetic programming, Expert Systems with Applications, 29, 41, 10.1016/j.eswa.2005.01.003

Paleologo, 2010, Subagging for credit scoring models, European Journal of Operational Research, 201, 490, 10.1016/j.ejor.2009.03.008

Piramuthu, 1999, Financial credit-risk evaluation with neural and neuro fuzzy systems, European Journal of Operational Research, 112, 310, 10.1016/S0377-2217(97)00398-6

Ravi, 2008, Threshold accepting trained principal component neural network and feature subset selection: application to bankruptcy prediction in banks, Applied Soft Computing, 8, 1539, 10.1016/j.asoc.2007.12.003

Salchenberger, 1992, Neural networks: a tool for predicting thrift failures, Decision Sciences, 23, 899, 10.1111/j.1540-5915.1992.tb00425.x

Sinkey, 1975, A multivariate statistical analysis of the characteristics of problem banks, Journal of Finance, 30, 21, 10.1111/j.1540-6261.1975.tb03158.x

Sustersic, 2009, Consumer credit scoring models with limited data, Expert Systems with Applications, 36, 4736, 10.1016/j.eswa.2008.06.016

Swicegood, 2001, Off-site monitoring for predicting, bank under performance: a comparison of neural networks, discriminant analysis and professional human judgment, Intelligent Systems in Accounting, Finance and Management, 10, 169

Tam, 1992, Predicting bank failures: a neural network approach, Decision Sciences, 23, 926

Tam, 1991, Neural network models and the prediction of bank bankruptcy, Omega, 19, 429, 10.1016/0305-0483(91)90060-7

Tan, 2001, A study on using artificial neural networks to develop an early warning predictor for credit union financial distress with comparison to the probit model, Managerial Finance, 27, 56, 10.1108/03074350110767141

Thomas, 2000, A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers, International Journal of Forecasting, 16, 149, 10.1016/S0169-2070(00)00034-0

Tong, 2012, Mixture cure models in credit scoring: if and when borrowers default, European Journal of Operational Research, 218, 132, 10.1016/j.ejor.2011.10.007

Trippi, 1996

Tsai, 2008, Using neural network ensembles for bankruptcy prediction and credit scoring, Expert Systems with Applications, 34, 2639, 10.1016/j.eswa.2007.05.019

Tsai, 2009, The consumer loan default predicting model – an application of DEA-DA and neural network, Expert Systems with Applications, 36, 11682, 10.1016/j.eswa.2009.03.009

Tseng, 2005, A quadratic interval logit model for forecasting bankruptcy, Omega, 33, 85, 10.1016/j.omega.2004.04.002

Tsukuda, 1994, Predicting Japanese corporate bankruptcy in terms of finance data using neural network, Computers and Industrial Engineering, 27, 445, 10.1016/0360-8352(94)90330-1

Vellido, 1999, Neural networks in business: a survey of applications (1992–1998), Expert Systems with Applications, 17, 51, 10.1016/S0957-4174(99)00016-0

West, 1985, A factor analytic approach to bank condition, Journal of Banking and Finance, 9, 253, 10.1016/0378-4266(85)90021-4

West, 2000, Neural network credit scoring models, Computers and Operations Research, 27, 1131, 10.1016/S0305-0548(99)00149-5

West, 2005, Neural network ensemble strategies for financial decision applications, Computers and Operations Research, 32, 2543, 10.1016/j.cor.2004.03.017

Wilson, 1994, Bankruptcy prediction using neural networks, Decision Support Systems, 11, 545, 10.1016/0167-9236(94)90024-8

Yang, 1999, Probabilistic neural networks in bankruptcy prediction, Journal of Business Research, 44, 67, 10.1016/S0148-2963(97)00242-7

Yildiz, 2009, Predicting bank bankruptcies with neuro fuzzy method, Journal of BRSA Banking and Financial Markets, 3, 9

Yildiz, 2001, Prediction of financial failure with artificial neural network technology and an empirical application on publicly held companies, Istanbul Stock Exchange Review, 5, 47

Yu, 2008, Credit risk assessment with a multistage neural network ensemble learning approach, Expert Systems with Applications, 34, 1434, 10.1016/j.eswa.2007.01.009

Zadeh, 1965, Fuzzy sets, Information and Control, 8, 338, 10.1016/S0019-9958(65)90241-X

Zhang, 1999, Artificial neural networks in bankruptcy prediction: general framework and cross-validation analysis, European Journal of Operational Research, 116, 16, 10.1016/S0377-2217(98)00051-4

Zhang, 1998, Forecasting with artificial neural networks: the state of the art, International Journal of Forecasting, 14, 35, 10.1016/S0169-2070(97)00044-7