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Differentiating between good credits and bad credits using neuro-fuzzy systems. (English) Zbl 1087.91515
Summary: To evaluate consumer loan applications, loan officers use many techniques such as judgmental systems, statistical models, or simply intuitive experience. In recent years, fuzzy systems and neural networks have attracted the growing interest of researchers and practitioners. This study compares the performance of artificial neuro-fuzzy inference systems (ANFIS) and multiple discriminant analysis models to screen potential defaulters on consumer loans. Using a modeling sample and a test sample, we find that the neuro-fuzzy system performs better than the multiple discriminant analysis approach to identify bad credit applications. Further, neuro-fuzzy systems have many advantages over traditional computational methods. Neuro-fuzzy system models are flexible, more tolerant of imprecise data, and can model non-linear functions of arbitrary complexity.

91B30Risk theory, insurance
Full Text: DOI
[1] Altman, E.; Marco, G.; Varetto, F.: Corporate distress diagnosis: comparisons using linear discriminant analysis and neural networks. Journal of banking and finance 18, 505-529 (1994)
[2] Coats, P.; Fant, F.: Recognizing financial distress patterns using a neural network tool. Financial management 22, No. 3, 142-155 (1993)
[3] Desai, V.; Crook, J.; Overstreet, G.: A comparison of neural networks and linear scoring models in the credit union environment. European journal of operations research 95, No. 1, 24-37 (1996) · Zbl 0955.90506
[4] Fabozzi, F., 1996. Bond Markets, Analysis and Strategies, Vol. 2. Prentice-Hall, Englewood Cliffs, NJ
[5] Fahlman, S.E., 1988. Faster-learning variations on back propagation: An empirical study. In: Proceedings of the 1988 Connectionist Models Summer School. pp. 38--51
[6] Gallager, R. G.: Information theory and reliable communication. (1968) · Zbl 0198.52201
[7] Goldberg, D. E.: Genetic algorithms in search, optimization, and machine learning. (1989) · Zbl 0721.68056
[8] Hair, J. F.; Anderson, R. E.; Tatham, R. L.: Multivariate data analysis. (1991)
[9] Hamilton, D.; Kelley, K.; Culbert, C.: State-of-the-art-practice in knowledge-based system verification and validation. Expert systems with applications 4, 403-410 (1991)
[10] Jang, J.-S.R., Gulley, N., 1995. Fuzzy Logic ToolBox. The Math Works, Foreword
[11] Jang, J.-S.R., Gulley, N., 1998. Fuzzy Logic ToolBox. The Math Works, Foreword
[12] Jang, J. -S.R.: ANFIS: adaptive-network-based fuzzy inference systems. IEEE transactions on systems, man, and cybernetics 23, No. 3, 665-685 (1993)
[13] Jang, J. -S.R.; Sun, C. T.; Mizutani, E.: Neuro-fuzzy and soft computing, Matlab curriculum series. (1997)
[14] Jensen, Herbert, 1992. Using neural networks for credit scoring. Managerial Finance 18 (6), 15--26
[15] Klassen, M.S., Pao, Y.-H., 1988. Characteristics of the functional-link net: A higher order delta rule net. IEEE Proceedings of the International Conference On Neural Networks
[16] Kohavi, R., John, G.H., 1995. Wrappers for feature subset selection. http://robotics.stanford.edu/ronnyk · Zbl 0904.68143
[17] Lacher, R.; Coats, P.; Sharma, S.; Fants, F.: A neural network for classifying the financial health of a firm. European journal of operations management 85, No. 1, 53-65 (1995) · Zbl 0910.90016
[18] Nelson, J.: Consumer bankruptcy and chapter choice: state panel evidence. Contemporary economic policy 17, No. 4, 552-566 (1999)
[19] Pao, Y.-H., 1989. Adaptive Pattern Recognition and Neural Networks. Addison-Wesley, Reading, MA, pp. 197--222
[20] Parker, D.B., 1987. Optimal algorithms for adaptive networks: Second-order back propagation, second-order direct propagation, and second-order Hebbian learning. In: Proceedings of IEEE International Conference on Neural Networks. pp. 593--600
[21] Piramuthu, S.: Financial credit-risk evaluation with neural and neuro-fuzzy systems. European journal of operational research 112, No. 2, 310-312 (1999)
[22] Salchenberger, L.; Minecinar, E.; Lash, N. A.: Neural networks: A new tool for predicting thrift failures. Decision sciences 23, 899-916 (1992)
[23] Skapura, D.: Building neural networks. (1996)
[24] Sugeno, M.; Kang, G. T.: Structure identifications of fuzzy model. Fuzzy sets and systems 28, 15-33 (1988) · Zbl 0652.93010
[25] Takagi, T., Sugeno, M., 1993. Derivation of fuzzy control rules from human operator’s control actions. In: Proceedings of the IFAC Symposium on Fuzzy Information, Knowledge Representation and Decision Analysis. pp. 55--60
[26] Takagi, T.; Sugeno, M.: Fuzzy identification of systems and its applications to modeling and control. IEEE transactions on systems, man, and cybernetics 15, 116-132 (1985) · Zbl 0576.93021
[27] Tam, K.; Kiang, M. Y.: Managerial applications of neural networks: the case of bank failure predictions. Management science 38, 926-947 (1992) · Zbl 0763.90062
[28] Weigend, A.S., Rumelhart, D.E., Huberman, B.A., 1990. Backpropagation, weight elimination and time series prediction. In: Proceedings of the 1990 Connectionist. Models Summer School. pp. 105--116
[29] Weigend, A.S., Rumelhart, D.E., Huberman, B.A., 1991. Generalization by weight elimination with application to forecasting. Advances in neural information processing systems III, 875--882
[30] Wismer, D.A., Chattergy, R., 1978. Introduction to Nonlinear Optimization: A Problem Solving Approach. North-Holland, Amsterdam, pp. 139--162 · Zbl 0378.90052
[31] Zadeh, L.A., 1995. In: Jang, J.-S.R., Gulley, N. (Eds.), Fuzzy Logic ToolBox. The Math Works Inc., Foreword, 1995 · Zbl 0925.93483
[32] Zhang, G.; Hu, M.; Patuwo, B.; Indro, D.: Artificial neural networks in bankruptcy prediction: general framework and cross-validation analysis. European journal of operational research 116, 16-32 (1999) · Zbl 0995.62506