Credit Risk Assessment Utilizing Data Reduction Technique for Individual Loaning in Financial Institutes (Case Study: Tejarat Bank, Rasht, Iran)

摘要

Because of the nature of the financial and economic activities and they are practically accompanied with a degree of risk., banks are usually dealing with many risks, including operational, marketing, interest rate, etc. Since, credit risk has significant effects on financial banks activities in terms of loaning profits, the risk of repayment individual loans has been investigated in this research work. Two well-known regression models of Probit and Logistic have been developed based on nine extracted factors which have been investigated during the offering of loans according to the possibility of late or non-repayment. In order to minimize inter-correlation and extracting high-independency factors, the statistical technique of Principal Component Analysis (PCA), categorized as a data reduction technique, has been utilized and three factors out of nine have been omitted. One of Tejarat bank branches in the Iranian Northern Province of Guilan has been selected as case study to gather experimental data for assessing the credit risk of individual bank investors. The results of model validation revealed that the implementation of PCA method can improve the accuracy of models' outputs and Probit regression model has better results rather than Logit one.