Assessing Credit Risk in the Banking System Using Data Mining Techniques

Subject Areas : مدیریت

Nima  Hamta ¹ (Assistant Professor, Department of Mechanical Engineering (Manufacturing), Arak University of Technology, Arak, Iran)
Mohammad  Ehsanifar ² (Assistant Professor, Department of Industrial Engineering, Arak Branch, Islamic Azad University, Arak, Iran)
Bahareh  Mohammadi ³ (M.Sc., Department of Industrial Engineering, Arak Branch, Islamic Azad University, Arak, Iran)

Received: 2020-10-18 Accepted : 2020-10-18 Published : 2019-02-20

Keywords: Data mining, Clustering, Neural network, Credit ranking, Supporter Vector Machine,

Abstract :

A credit risk is the risk of default on a debt that may arise from a borrower failing to make required payments. The objective of this paper is recognition of the factors that effect on credit risk and presenting a model for prediction of credit risk and legal customer credit ranking that are applicant of Sepah bank facilities in Dezfool city and the method of Clustering, Neural Network and Supporter Vector Machine has been used in the current study. Accordingly necessary investigations have been done on financial and nonfinancial data by means of a simple random sample of 200 legal customers that were applicant of bank facilities. In the this paper, 27 descriptive variable that include financial and nonfinancial variables were investigated and finally available variables 8 effective variables on credit risk were selected by means of bank experts judges that were separated by data collection Clustering method in to some groups (Clusters) in the someway that data in one Cluster were considering other points in other Clusters had more similarity. Also selected variables with 3 layers perceptron Neural Network input vector entered the model and finally by means of Support Vector Machine was presented in order to bank legal customers’ financial operation prediction. The obtained results of Neural Network model and Supporter Machine indicate that Neural Network model has mire efficiency in legal customers’ credit risk prediction and credit ranking.