Provide a method for validation of bank customers using data mining techniques (Case study: Bank Sepah)

Abstract

The financial institutions and the banks have considerable damages from non-creditworthy customers. Non-creditworthy customers are the clients that regarding the service they receive, do not pay the bank payment on time. The failure of some customers to pay their debt on time will lead to bankruptcy of some institutions. . Also lending financial institution to creditworthy customers causes to keep their money in that institution and will follow by a good progress. A model that can predict the customer behavior with an acceptable accuracy can be financial and service provider companies as a decision support tools that are very helpful and efficient for banks. By using the information related to bank clients and using data mining process, the credit ratings and classification of creditworthy and noncredit worthy applicants, without personal judgment and based on intelligent systems can be performed. Since the banking system is one of the factor that affect on the economic development of country, the bank low capital against the total asset value is the factor that can lead to their bankruptcy in the event of non-repayment. Therefore, the clients’ credit assessment before lending is very important, and in order to reduce the risk of repayment, the banks are forced to assess the clients’ credit. In this article by using a logit regression, an applicable model is suggested for bank clients validation (Case Study: Sepah bank) it is hoped that can be fruitful