ارزيابي ريسك اعتباري در سيستم‌هاي بانكي با استفاده از تكنيك‌هاي داده‌كاوي

Assessing Credit Risk in the Banking System Using Data Mining Techniques

اﻳﻦ ﻣﻘﺎﻟﻪ ﺑﺎ ﻫﺪف ﺷﻨﺎﺳﺎﻳﻲ ﻋﻮاﻣﻞ ﻣﺆﺛﺮ ﺑﺮ رﻳﺴﻚ اﻋﺘﺒﺎري و اراﺋﻪ ﻣﺪﻟﻲ ﺟﻬﺖ ﭘﻴﺶﺑﻴﻨـﻲ رﻳﺴـﻚ اﻋﺘﺒـﺎري و رﺗﺒـﻪ ﺑﻨـﺪي اﻋﺘﺒـﺎري ﻣﺸـﺘﺮﻳﺎن ﺣﻘـﻮﻗﻲ ﻣﺘﻘﺎﺿﻲ ﺗﺴﻬﻴﻼت ﺑﺎﻧﻚ ﺳﭙﻪ ﺷﻬﺮﺳﺘﺎن دزﻓﻮل، ﺑﺎ اﺳﺘﻔﺎده از روشﻫﺎي ﺧﻮﺷﻪﺑﻨﺪي، ﺷﺒﻜﻪ ﻋﺼﺒﻲ و ﻣﺎﺷﻴﻦ ﺑﺮدار ﭘﺸﺘﻴﺒﺎن اﻧﺠﺎم ﮔﺮﻓﺘـﻪ اﺳـﺖ در اﻳـﻦ ﻣﻘﺎﻟﻪ، 27 ﻣﺘﻐﻴﺮ ﺗﻮﺿﻴﺢدﻫﻨﺪه ﺷﺎﻣﻞ ﻣﺘﻐﻴﺮﻫﺎي ﻣﺎﻟﻲ و ﻏﻴﺮﻣﺎﻟﻲ ﻣﻮرد ﺑﺮرﺳﻲ ﻗﺮار ﮔﺮﻓﺖ ﻛﻪ از ﺑﻴﻦ اﻳﻦ ﻣﺘﻐﻴﺮﻫﺎ، 8 ﻣﺘﻐﻴﺮ ﺗﺄﺛﻴﺮﮔﺬار ﺑﺮ رﻳﺴﻚ اﻋﺘﺒـﺎري اﻧﺘﺨﺎب ﮔﺮدﻳﺪ ﻛﻪ ﺑﻪ وﺳﻴﻠﻪ روش ﺧﻮﺷﻪﺑﻨﺪي ﻣﺠﻤﻮﻋﻪ دادهﻫﺎ ﺑﻪ ﺧﻮﺷﻪﻫﺎ دﺳﺘﻪﺑﻨﺪي ﺷﺪﻧﺪ. ﻫﻢﭼﻨﻴﻦ ﻣﺘﻐﻴﺮﻫﺎي اﻧﺘﺨﺎﺑﻲ ﺑﻪ ﻋﻨﻮان ﺑﺮدار ورودي ﺷـﺒﻜﻪ ﻋﺼﺒﻲ ﭘﺮﺳﭙﺘﺮون 3 ﻻﻳﻪ وارد ﻣﺪل ﺷﺪ و در ﻧﻬﺎﻳﺖ ﺑﺎ اﺳﺘﻔﺎده از ﻣﺎﺷﻴﻦ ﺑﺮدار ﭘﺸﺘﻴﺒﺎن، ﺑﻪ ﻣﻨﻈﻮر ﭘﻴﺶﺑﻴﻨﻲ ﻋﻤﻠﻜﺮد ﻣﺎﻟﻲ ﻣﺸﺘﺮﻳﺎن ﺣﻘﻮﻗﻲ ﺑﺎﻧـﻚ اراﺋـﻪ ﮔﺮدﻳﺪ. ﻧﺘﺎﻳﺞ ﺣﺎﺻﻞ از ﻣﺪل ﺷﺒﻜﻪ ﻋﺼﺒﻲ و ﻣﺎﺷﻴﻦ ﺑﺮدار ﭘﺸﺘﻴﺒﺎن ﺣﺎﻛﻲ از آن اﺳﺖ ﻛﻪ ﻣﺪل ﺷﺒﻜﻪ ﻋﺼﺒﻲ در ﭘـﻴﺶ ﺑﻴﻨـﻲ رﻳﺴـﻚ اﻋﺘﺒـﺎري ﻣﺸـﺘﺮﻳﺎن ﺣﻘﻮﻗﻲ و رﺗﺒﻪﺑﻨﺪي اﻋﺘﺒﺎري از ﻛﺎراﻳﻲ ﺑﻴﺸﺘﺮي ﺑﺮﺧﻮدار اﺳﺖ.

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.