Credit Ranking of Parsian Bank legal Customers

In this research we are going to develop a model for evaluating the credit risk and credit ranking through customers in Parsian Bank by the help of Logit and Probit Regression and GMDH neural network methods. This model will be based on the qualitative and financial data of a random sample of 400 customers receiving credit facilities. After analyzing the credit files of each customers, we identified 11 explanatory variables including qualitative and financial aspects as follows: type of security, type of the workplace owner ship, cooperation background, capital, current ratio, quick ratio, the ratio of current asset to total assets, total asset turn over, turnover, current capital turnover, dept ratio and stock holder equity ratio that have significant impact on credit risk. Findings of the study corroborate the economic and financial theories of effective factors influencing credit risk and indicate that neural network model produce more efficient and precise results than the other popular economic models like Logit and Probit. Also, money explanatory variables such as the type of collateral and dept ratio have the most effects and the cooperation background, current ratio, stock holder equity have a usual effect and the rest variables are less effective.