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Information Journal Paper

Title

CREDIT RISK MANAGEMENT IN AGRICULTURAL BANK OF MAMASANI USING NEURAL NETWORK MODEL

Pages

  103-119

Abstract

 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.

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    APA: Copy

    HOSSEINI, SEYED ABDOLKHALEGH, & ZIBAEI, MANSOOR. (2015). CREDIT RISK MANAGEMENT IN AGRICULTURAL BANK OF MAMASANI USING NEURAL NETWORK MODEL. AGRICULTURAL ECONOMICS: IRANIAN JOURNAL OF AGRICULTURAL ECONOMICS (ECONOMICS AND AGRICULTURE JOURNAL), 9(2 ), 103-119. SID. https://sid.ir/paper/124334/en

    Vancouver: Copy

    HOSSEINI SEYED ABDOLKHALEGH, ZIBAEI MANSOOR. CREDIT RISK MANAGEMENT IN AGRICULTURAL BANK OF MAMASANI USING NEURAL NETWORK MODEL. AGRICULTURAL ECONOMICS: IRANIAN JOURNAL OF AGRICULTURAL ECONOMICS (ECONOMICS AND AGRICULTURE JOURNAL)[Internet]. 2015;9(2 ):103-119. Available from: https://sid.ir/paper/124334/en

    IEEE: Copy

    SEYED ABDOLKHALEGH HOSSEINI, and MANSOOR ZIBAEI, “CREDIT RISK MANAGEMENT IN AGRICULTURAL BANK OF MAMASANI USING NEURAL NETWORK MODEL,” AGRICULTURAL ECONOMICS: IRANIAN JOURNAL OF AGRICULTURAL ECONOMICS (ECONOMICS AND AGRICULTURE JOURNAL), vol. 9, no. 2 , pp. 103–119, 2015, [Online]. Available: https://sid.ir/paper/124334/en

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