Measuring Credit Risk of Bank Customers Using Artificial Neural Network

Abstract

In many studies, the relationship between development of financial markets and economic growth has been proved. Credit risk is one of problems which banks are faced with it while doing their tasks. Credit risk means the probability of non-repayment of bank financial facilities granted to investors. If the credit risk decreases, banks will be more successful in performing their duties and have greater effect on economic growth of the country. Credit rating of customers and identifying good and bad customers, help banks lend to their good payers and hereby, they reduce probability of non-repayment. This paper aims to identify classification criteria for good customers and bad customers in Iranian banks. This study can classified in applied studies group and the research strategy is descriptive. Artificial neural network technique is used for financial facilities applicants' credit risk measurement and the calculations have been done by using SPSS and MATLAB software. Number of samples was 497 and 18 variables were used to identify good customers from bad customers. The results showed that, individual loan frequency and amount of loan had most important effect and also status of customer’s bank account, history of customer relationship with bank and received services had least important effect in identifying classification criteria of good and bad customers. The major contribution of this paper is specifying the most important determinants for rating of customers in Iran’s banking sector.