A Hybrid Intelligent Classification Model Based on Multilayer Perceptron Neural Networks and Fuzzy Regression for Credit Scoring Problems
Authors
Abstract
Financial crises in banking systems are due to inability to manage credit risks. Credit scoring is one of the risk management techniques that analyze the borrower's risk. In this paper, using the advantages of computational intelligence as well as soft computing methods, a new hybrid approach is proposed in order to improve credit risk management. In the proposed method, for modeling in uncertainty conditions, parameters of the neural network, including weights and errors, are considered in the form of fuzzy numbers. In this method, the underlying system is firstly modeled using neural networks and then, using fuzzy inferences, the optimal decision will be determined with the highest degree of superiority. Empirical results of using the proposed method indicate the efficiency and high accuracy of this method in analyzing credit rating problems.
Keywords
1. Lee , T. S., Chiu, C. C., Lu, C. J., and Chen, I. F. “Credit Scoring using the Hybrid Neural Discriminant Technique”, Journal of Expert Systems with Applications, Vol. 23, pp. 245-254, 2002.
2. Gestel, T. V., and Baesens, B., Credit Risk Management, Published in the United States by Oxford University Press Inc., New York, 2009.
3. Westgaard, S., and Van der Wijst, N., “Default Probabilities in a Corporate Bank Portfolio: A Logistic Model Approach”, European Journal of Operational Research, Vol. 135, No. 2, pp. 338-349, 2001.
4. Charitou, A., Neophytou, E., and Charalambous, C., “Predicting Corporate Failure: Empirical Evidence for the UK”, European Accounting Review, Vol. 13, No. 3, pp. 465-497, 2004.
5. Lee, G., Sung, T. K., and Chang, N., “Dynamics of Modeling in Data Mining: Interpretive Approach to Bankruptcy Prediction”, Journal of Management Information Systems, Vol. 16, pp. 63-85, 1999.
6. Bellotti, T., and Crook, J., “Support Vector Machines for Credit Scoring and Discovery of Significant Features”, Expert Systems with Applications, Vol. 36, pp. 3302-3308, 2008.
7. Sharma, S., Applied Multivariate Techniques, New York, NY: Wiley, SPSS 1997-Statistic Modeling for Windows 95/98/NT, SPSS Inc, 1998.
8. Baesens, B., Verstraeten, G., Poel, D. V. d., Egmont-Petersen, M., Kenhove, P. V., and Vanthienen, J., “Bayesian Network Classifiers for Identifying the Slope of the Customer -lifecycle of Long-life Customers”, European Journal of Operational Research, Vol. 156, pp. 508-523, 2004.
9. Sun, L., and Shenoy, P. P., “Using Bayesian Networks for Bankruptcy Prediction: Some Methodological Issues”, European Journal of Operational Research, Vol. 180, pp. 738-753, 2007.
10. Lee, T. S., and Chen, I. F., “A Two-stage Hybrid Credit Scoring Model using Artificial Neural Networks and Multivariate Adaptive Regression Splines”, Expert Systems with Applications, Vol. 28, pp. 743-752. 2005.
11. Kline, D. M., “Two-group Classification using the Bayesian Data Reduction Algorithm”, Complexity, Vol. 15, pp. 43-49, 2010.
12. Huang, C. L., Chen, M. C., and Wang, C. J., “Credit Scoring with a Data Mining Approach Based on Support Vector Machines”, Expert Systems with Applications, Vol. 33, pp. 847-856, 2007.
13. Huang, J. -J., Tzeng, G. -H., and Ong, C. -S., “Two-stage Genetic Programming (2SGP) for the Credit Scoring Model”, Applied Mathematics and Computation, Vol. 174, No. 2, pp. 1039-1053, 2006.
14. Chen, M. C., and Huang, S. H., “Credit Scoring and Rejected Instances Reassigning Through Evolutionary Computation Techniques”, Expert Systems with Applications, Vol. 24, pp. 433-441, 2003.
15. Khashei, M., Bijari, M., and Raissi, Gh. A., “Improvement of Auto-regressive Integrated Moving Average Models using Fuzzy Logic and Artificial Neural Networks (ANNs)”, Neurocomputing, Vol. 72, pp. 956-967, 2009.
16. Celikyilmaz, A., and Turksen, I. B., “Enhanced Fuzzy System Models with Improved Fuzzy Clustering Algorithm”, IEEE Transactions on Fuzzy Systems, Vol. 16, pp. 779-794, 2008.
17. Khashei, M., Bijari, M., and Hejazi, S. R., “Combining Seasonal ARIMA Models with Computational Intelligence Techniques for Time Series Forecasting”, Soft Computing, Vol. 16, pp. 1091-1105, 2012.
18. Lee, T. S., Chiu, C. C., Lu, C. J., and Chen, I. F., “Credit Scoring using the Hybrid Neural Discriminate Technique”, Expert Systems with Applications, Vol. 23, pp. 245-254, 2002.
19. Hsieh, N -Ch., “Hybrid Mining Approach in the Design of Credit Scoring Models”, Expert Systems with Applications, Vol. 28, pp. 655-665, 2005.
20. Hung, C. L., Chen, M. C., and Wang, C. J., “Credit Scoring with a Data Mining Approach Based on Support Vector Machines”, Expert Systems with Applications, Vol. 33, pp. 847-856, 2007.
21. Li, J., Wei, L., Li, G., and Xu, W., “An Evolution Strategy-based Multiple Kernels Multi-criteria Programming Approach: The Case of Credit Decision Making”, Decision Support Systems, Vol. 51, pp. 292-298, 2011.
22. Celikyilmaz, A., and Turksen, I. B., “Uncertainty Modeling of Improved Fuzzy Functions with Evolutionary Systems”, IEEE Transactions on Systems, Man, and Cybernetics, Vol. 38, pp. 1098-1110, 2008.
23. Chen, F. -L., and Li, F. -Ch., “Combination of Feature Selection Approaches with SVM in Credit Scoring”, Expert Systems with Applications, Vol. 37 pp. 4902-4909, 2010.
24. Bijak, K., and Thomas, L. C., “Does Segmentation Always Improve Model Performance in Credit Scoring”, Expert Systems with Applications, Vol. 39, No. 3, pp. 2433-2442, 2012.
25. Kim, K. S., and Han, I., “The Cluster-indexing Method for Case based Reasoning using Self-organizing Maps and Learning Vector Quantization for Bond Rating Cases”, Expert Systems with Applications, Vol. 21, pp. 147-156, 2001.
26. Park, C., and Han, I., “A Case-based Reasoning with the Feature Weights Derived by Analytic Hierarchy Process for Bankruptcy Prediction”, Expert Systems with Applications, Vol. 23, No. 3, pp. 255-264, 2002.
27. Ahn, H., and Kim, K. -J., “Bankruptcy Prediction Modeling with Hybrid Case-based Reasoning and Genetic Algorithms Approach”, Applied Soft Computing, Vol. 9, pp. 599-607, 2009.
28. Capotorti, A., and Barbanera, E., “Credit Scoring Analysis using a Fuzzy Probabilistic Rough Set Model”, Computational Statistics and Data Analysis, Vol. 56, No. 4, pp. 981-994, 2012.
29. Akkoc, S., “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, Vol. 222, pp. 168-178, 2012.
30. Yu, L., Wang, S., and Lai, K., “An Intelligent-agent-based Fuzzy Group Decision Making Model for Financial Multicriteria Decision Support: The Case of Credit Scoring”, European Journal of Operational Research, Vol. 195, pp. 942-959, 2009.
31. Chen, W., Ma, C., and Ma, L., “Mining the Customer Credit using Hybrid Support Vector Machine Technique”, Expert Systems with Applications, Vol. 36, pp. 7611-7616, 2009.
32. Lee, S., and Choi, W. S., “A Multi-industry Bankruptcy Prediction Model using Back-propagation Neural Network and Multivariate Discriminant Analysis”, Expert Systems with Applications, Vol. 40, pp. 2941-2946, 2013.
33. Zhao, Z., Xu, S., Kong, B. H., Kabir, M. M. J., Liu, Y., and Wasinger, R., “Investigation and Improvement of Multi-layer Perceptron Neural Networks for Credit Scoring”, Expert Systems with Applications, Vol. 42, pp. 3508-3516, 2015.
34. Sohn, S. Y., Kim, H. D., and Yoon, H. J., “Technology Credit Scoring Model with Fuzzy Logistic Regression”, Applied Soft Computing, Vol. 43, pp. 150-158, 2016.
35. Alaraj, M., and Abbod, F., “A New Hybrid Ensemble Credit Scoring Model Based on Classifiers Consensus System Approach”, Expert Systems With Applications, Vol. 64, pp. 36-55, 2016.
36. Neto, R., Adeodato, P. J., and Salgado, C. A., “A Framework for Data Transformation in Credit Behavior Scoring Applications Based on Model Driven Development”, Expert Systems With Applications, Vol. 72, pp. 1-13, 2016.
37. Khashei, M., and Bijari, M., “Hybridization of the Probabilistic Neural Networks with Feed-forward Neural Networks for Forecasting”, Engineering Applications of Artificial Intelligence, Vol. 25, pp. 1277-1288, 2012.
38. Tanaka, H., “Fuzzy Data Analysis by Possibility Linear Models”, Fuzzy Sets and Systems, Vol. 24, pp. 363-375, 1987.
39. Khashei, M., Hejazi, S. R., and Bijari, M., “A New Hybrid Artificial Neural Networks and Fuzzy Regression Model for Time Series Forecasting”, Fuzzy Set and Systems, Vol. 159, pp. 769-786, 2008.
2. Gestel, T. V., and Baesens, B., Credit Risk Management, Published in the United States by Oxford University Press Inc., New York, 2009.
3. Westgaard, S., and Van der Wijst, N., “Default Probabilities in a Corporate Bank Portfolio: A Logistic Model Approach”, European Journal of Operational Research, Vol. 135, No. 2, pp. 338-349, 2001.
4. Charitou, A., Neophytou, E., and Charalambous, C., “Predicting Corporate Failure: Empirical Evidence for the UK”, European Accounting Review, Vol. 13, No. 3, pp. 465-497, 2004.
5. Lee, G., Sung, T. K., and Chang, N., “Dynamics of Modeling in Data Mining: Interpretive Approach to Bankruptcy Prediction”, Journal of Management Information Systems, Vol. 16, pp. 63-85, 1999.
6. Bellotti, T., and Crook, J., “Support Vector Machines for Credit Scoring and Discovery of Significant Features”, Expert Systems with Applications, Vol. 36, pp. 3302-3308, 2008.
7. Sharma, S., Applied Multivariate Techniques, New York, NY: Wiley, SPSS 1997-Statistic Modeling for Windows 95/98/NT, SPSS Inc, 1998.
8. Baesens, B., Verstraeten, G., Poel, D. V. d., Egmont-Petersen, M., Kenhove, P. V., and Vanthienen, J., “Bayesian Network Classifiers for Identifying the Slope of the Customer -lifecycle of Long-life Customers”, European Journal of Operational Research, Vol. 156, pp. 508-523, 2004.
9. Sun, L., and Shenoy, P. P., “Using Bayesian Networks for Bankruptcy Prediction: Some Methodological Issues”, European Journal of Operational Research, Vol. 180, pp. 738-753, 2007.
10. Lee, T. S., and Chen, I. F., “A Two-stage Hybrid Credit Scoring Model using Artificial Neural Networks and Multivariate Adaptive Regression Splines”, Expert Systems with Applications, Vol. 28, pp. 743-752. 2005.
11. Kline, D. M., “Two-group Classification using the Bayesian Data Reduction Algorithm”, Complexity, Vol. 15, pp. 43-49, 2010.
12. Huang, C. L., Chen, M. C., and Wang, C. J., “Credit Scoring with a Data Mining Approach Based on Support Vector Machines”, Expert Systems with Applications, Vol. 33, pp. 847-856, 2007.
13. Huang, J. -J., Tzeng, G. -H., and Ong, C. -S., “Two-stage Genetic Programming (2SGP) for the Credit Scoring Model”, Applied Mathematics and Computation, Vol. 174, No. 2, pp. 1039-1053, 2006.
14. Chen, M. C., and Huang, S. H., “Credit Scoring and Rejected Instances Reassigning Through Evolutionary Computation Techniques”, Expert Systems with Applications, Vol. 24, pp. 433-441, 2003.
15. Khashei, M., Bijari, M., and Raissi, Gh. A., “Improvement of Auto-regressive Integrated Moving Average Models using Fuzzy Logic and Artificial Neural Networks (ANNs)”, Neurocomputing, Vol. 72, pp. 956-967, 2009.
16. Celikyilmaz, A., and Turksen, I. B., “Enhanced Fuzzy System Models with Improved Fuzzy Clustering Algorithm”, IEEE Transactions on Fuzzy Systems, Vol. 16, pp. 779-794, 2008.
17. Khashei, M., Bijari, M., and Hejazi, S. R., “Combining Seasonal ARIMA Models with Computational Intelligence Techniques for Time Series Forecasting”, Soft Computing, Vol. 16, pp. 1091-1105, 2012.
18. Lee, T. S., Chiu, C. C., Lu, C. J., and Chen, I. F., “Credit Scoring using the Hybrid Neural Discriminate Technique”, Expert Systems with Applications, Vol. 23, pp. 245-254, 2002.
19. Hsieh, N -Ch., “Hybrid Mining Approach in the Design of Credit Scoring Models”, Expert Systems with Applications, Vol. 28, pp. 655-665, 2005.
20. Hung, C. L., Chen, M. C., and Wang, C. J., “Credit Scoring with a Data Mining Approach Based on Support Vector Machines”, Expert Systems with Applications, Vol. 33, pp. 847-856, 2007.
21. Li, J., Wei, L., Li, G., and Xu, W., “An Evolution Strategy-based Multiple Kernels Multi-criteria Programming Approach: The Case of Credit Decision Making”, Decision Support Systems, Vol. 51, pp. 292-298, 2011.
22. Celikyilmaz, A., and Turksen, I. B., “Uncertainty Modeling of Improved Fuzzy Functions with Evolutionary Systems”, IEEE Transactions on Systems, Man, and Cybernetics, Vol. 38, pp. 1098-1110, 2008.
23. Chen, F. -L., and Li, F. -Ch., “Combination of Feature Selection Approaches with SVM in Credit Scoring”, Expert Systems with Applications, Vol. 37 pp. 4902-4909, 2010.
24. Bijak, K., and Thomas, L. C., “Does Segmentation Always Improve Model Performance in Credit Scoring”, Expert Systems with Applications, Vol. 39, No. 3, pp. 2433-2442, 2012.
25. Kim, K. S., and Han, I., “The Cluster-indexing Method for Case based Reasoning using Self-organizing Maps and Learning Vector Quantization for Bond Rating Cases”, Expert Systems with Applications, Vol. 21, pp. 147-156, 2001.
26. Park, C., and Han, I., “A Case-based Reasoning with the Feature Weights Derived by Analytic Hierarchy Process for Bankruptcy Prediction”, Expert Systems with Applications, Vol. 23, No. 3, pp. 255-264, 2002.
27. Ahn, H., and Kim, K. -J., “Bankruptcy Prediction Modeling with Hybrid Case-based Reasoning and Genetic Algorithms Approach”, Applied Soft Computing, Vol. 9, pp. 599-607, 2009.
28. Capotorti, A., and Barbanera, E., “Credit Scoring Analysis using a Fuzzy Probabilistic Rough Set Model”, Computational Statistics and Data Analysis, Vol. 56, No. 4, pp. 981-994, 2012.
29. Akkoc, S., “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, Vol. 222, pp. 168-178, 2012.
30. Yu, L., Wang, S., and Lai, K., “An Intelligent-agent-based Fuzzy Group Decision Making Model for Financial Multicriteria Decision Support: The Case of Credit Scoring”, European Journal of Operational Research, Vol. 195, pp. 942-959, 2009.
31. Chen, W., Ma, C., and Ma, L., “Mining the Customer Credit using Hybrid Support Vector Machine Technique”, Expert Systems with Applications, Vol. 36, pp. 7611-7616, 2009.
32. Lee, S., and Choi, W. S., “A Multi-industry Bankruptcy Prediction Model using Back-propagation Neural Network and Multivariate Discriminant Analysis”, Expert Systems with Applications, Vol. 40, pp. 2941-2946, 2013.
33. Zhao, Z., Xu, S., Kong, B. H., Kabir, M. M. J., Liu, Y., and Wasinger, R., “Investigation and Improvement of Multi-layer Perceptron Neural Networks for Credit Scoring”, Expert Systems with Applications, Vol. 42, pp. 3508-3516, 2015.
34. Sohn, S. Y., Kim, H. D., and Yoon, H. J., “Technology Credit Scoring Model with Fuzzy Logistic Regression”, Applied Soft Computing, Vol. 43, pp. 150-158, 2016.
35. Alaraj, M., and Abbod, F., “A New Hybrid Ensemble Credit Scoring Model Based on Classifiers Consensus System Approach”, Expert Systems With Applications, Vol. 64, pp. 36-55, 2016.
36. Neto, R., Adeodato, P. J., and Salgado, C. A., “A Framework for Data Transformation in Credit Behavior Scoring Applications Based on Model Driven Development”, Expert Systems With Applications, Vol. 72, pp. 1-13, 2016.
37. Khashei, M., and Bijari, M., “Hybridization of the Probabilistic Neural Networks with Feed-forward Neural Networks for Forecasting”, Engineering Applications of Artificial Intelligence, Vol. 25, pp. 1277-1288, 2012.
38. Tanaka, H., “Fuzzy Data Analysis by Possibility Linear Models”, Fuzzy Sets and Systems, Vol. 24, pp. 363-375, 1987.
39. Khashei, M., Hejazi, S. R., and Bijari, M., “A New Hybrid Artificial Neural Networks and Fuzzy Regression Model for Time Series Forecasting”, Fuzzy Set and Systems, Vol. 159, pp. 769-786, 2008.
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