:: year 10, Issue 34 (1-2018) ::
JMBR 2018, 10(34): 680-657 Back to browse issues page
Comparison Between Neural Network, Genetic Algorithm and Logit Models in Evaluating Consumer Credit Risk
Fethullah Tari1, Seyed Ahmad Ebrahimi 2, Seyed Jafar Mousavi3, Mahmoud Kalantari4
1- Allame Tabatabaei University
2- Researcher at National Research Institute For Science Policy
3- Entrepreneurship Education
4- Iran University of Economic Science
Abstract:   (1026 Views)

 The purpose of this study is to assess the credit rating methods of real customers (micro-credit recipients) of banks, by reviewing the financial records and characteristics of the applicantchr('39')s characteristics. In this research, the effectiveness of some methods (logit model, neural network, and genetic algorithm) is evaluated for accurate measurement of the Defaults. For this purpose, the information and financial and qualitative data of a random sample of 399 customers who have received facilities during the years 1387 to 1391 have been investigated. After reviewing the credit records of each of the customers, 12 explanatory variables were identified which, based on the logit test variables, credit history, six-month average account, employment status, amount of credit, monthly installments and repayment period, had a significant effect on default. The results of the evaluation of credit rating methods indicate that the performance of the neural network is much better than the Genetic and Logit models because the sensitivity is 82.92% and the specificity is 76.92%, and in general, this model has been able to 80% Predict default or non-default. Therefore, in order to reduce the bankchr('39')s credit risk, it is suggested that a structural adjustment based on the creation of a customer validation system based on the neural network is proposed.  

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Type of Study: Theoretical Article | Subject: Monetary Policy, Central Banking, and the Supply of Money and Credit (E5)
Received: 2016/12/13 | Accepted: 2018/06/26 | Published: 2018/07/25

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year 10, Issue 34 (1-2018) Back to browse issues page