Recently one of the main problems in banks and financial institutions is the risk of customers` credit risk that sometimes plays a key role in bankruptcy. It is obvious for credit risk management one useful way is dividing of banking customers into two groups; good pay customers and bad pay customers. Until now, variety of methods such as artificial neural networks, genetic algorithm, linear regression, logistic, probit and logit models have been used in the legal field to authenticate customers. In recent years has been more attention the logistic regression because of the high accuracy. In this research a logit model has been used to determine the probability of Mehr economy bank`s corporate customers default risk. In this model the dependent variable is the condition of customers` loan repayment and the explanatory variables are some financial ratios and corporate characteristics. Data collected from customers credit book from 2008 to 2010. Results show that some management and financial variables e.g. age, asset turnover ratio, bank credit history, Current Ratio, Average current account balance, Profit margins and interest rate are significant. Indeed Estimate provided Predicts Credit status of customers with 0.82 percent.

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