Credit Risk Measurement of Trusted Customers Using Logistic Regression and Neural Networks
الموضوعات :Gholamreza Khojasteh 1 , Saeed Daei Karimzadeh 2 , Hossein Sharifi Ranani 3
1 - Department of Management, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran
2 - Department of Economics, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran
3 - Department of Economics, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran
الکلمات المفتاحية: Receiver Operating Characteristic (ROC), Credit Risk, Neural Networks, Logistic regression,
ملخص المقالة :
The issue of credit risk and deferred bank claims is one of the sensitive issues of banking industry, which can be considered as the main cause of bank failures. In recent years, the economic slowdown accompanied by inflation in Iran has led to an increase in deferred bank claims that could put the country's banking system in serious trouble. Accordingly, the current paper presents a prediction model for credit risk of real customers of Qavamin Bank Branch in Shiraz, using a combined approach of logistic regression and neural network. Therefore, the necessary examinations were carried out on a sample of 351 individuals from the real customers of the bank in the period 2011-2012. According to the information available, 17 variables were extracted including financial and non-financial variables for classifying customers into well-balanced s and ill-balanced s. Among the variables, five effective variables on credit risk were selected using the parent forward stepwise selection technique, which was used to train neural networks with three neurons in the hidden layer. the optimum cutting point was selected based on the performance curve of the system and the results of the neural network output on the test data show that the accuracy of the combined model in the classifier of well-balanced customers is .89 and in the category of ill-balanced customers is .83 that is better than the results of logistic regression and in general, it is possible to estimate the accuracy of prediction.
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