This research has been done with the aim of identification of effective factors which influence credit risk and designing model for estimating credit risk of the farmers which have borrowed from an Agricultural Bank using Neural Network approach. For this purpose the necessary sample data on financial and non-financial information of 205farmers which received loan in Mamasani township (as multi-stage and cluster random simple) in 1386-1391 period was selected. In this research, 17 explanatory variables (include financial and non-financial variables) were selected and analyzed. The variables as well as the input vector three-layer perceptron neural network models were added to the model. The results indicated that the neural network model was able to estimated the observations with 95.5% efficiency, this indicates the high ability of the neural network model to predict credit risk of customers.
Hosseini, S. A., & Zibaei, M. (2015). Credit Risk Management in Agricultural Bank of Mamasani Using Neural Network Model. Agricultural Economics, 9(2), 103-119.
MLA
Seyed Abdolkhalegh Hosseini; Mansour Zibaei. "Credit Risk Management in Agricultural Bank of Mamasani Using Neural Network Model". Agricultural Economics, 9, 2, 2015, 103-119.
HARVARD
Hosseini, S. A., Zibaei, M. (2015). 'Credit Risk Management in Agricultural Bank of Mamasani Using Neural Network Model', Agricultural Economics, 9(2), pp. 103-119.
VANCOUVER
Hosseini, S. A., Zibaei, M. Credit Risk Management in Agricultural Bank of Mamasani Using Neural Network Model. Agricultural Economics, 2015; 9(2): 103-119.
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