Volume 37, Issue 2 (3-2019)                   JCME 2019, 37(2): 97-111 | Back to browse issues page

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Khashei M, Torbat S. A Hybrid Intelligent Classification Model Based on Multilayer Perceptron Neural Networks and Fuzzy Regression for Credit Scoring Problems. JCME. 2019; 37 (2) :97-111
URL: http://jcme.iut.ac.ir/article-1-629-en.html
1- Department of Industrial Engineering, Isfahan University of Technology, Isfshsn, Iran.
2- Department of Industrial Engineering, Isfahan University of Technology, Isfshsn, Iran. , S.Torbat@in.iut.ac.ir
Abstract:   (69 Views)

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.

Full-Text [PDF 527 kb]   (27 Downloads)    
Type of Study: Research | Subject: Special
Received: 2016/12/19 | Accepted: 2017/07/9

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