Credit Ranking of Parsian Bank legal Customers
Subject Areas : Labor and Demographic Economics
Keywords: accreditation, Credit risk, Logit and Probit, GMDH Neural Network,
Abstract :
In this research we are going to develop a model for evaluating the credit risk and credit ranking by customers in Parsian Bank by the help of the Logit and Probit regression and GMDH neural network methods. This model will be based on the qualitative and financial data a random sample of 400 customers receiving credit facilities. After analysis the credit files of each customers, we identified 11 explanatory variables including qualitative and financial aspects as follows: the type of security the type of the workplace owner ship, cooperation background, capital, current ratio, quick ratio, the ratio of current assent to total assents total asset turn over, turnover, current capital turnover, dept ratio an stock holder equity ratio that have significant impart on credit risk. The finding of the study corroborate the economic and financial theories of affection factors influencing credit risk, indicate that neural network model produce mare efficient and precise results than the other popular economic models like Logit and Probit. Also, a money explanatory variables, the type of collateral and dept ratio have the most effect, cooperation background, current ratio, stock holder equity have usual effect and the rest variables are less affection.
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