The current study seeks to provide a new solution for evaluation of banking system customers risk by integrating different scientific methodology. Evaluation of banking system customers risk in Iranian banks relies on experts judgment and fingertip rule. This type of evaluation resulted in high rate of postponed claims; therefore, designing new intelligent model for credit risk evaluation will be helpful, thus in this paper, we formulated an intelligent model by neural network and logistic regression that evaluated all individual customers credit risk without prejudice and discrimination. The result revealed that neural network and logistic regression have the same ability in predicting customer credit risk. Their ability in customer credit risk correct evaluation was nearly 79.50%. We suggested that both models could be used by all financial system as consultant model for customer credit risk prediction. The study also involved only one banking system credit customers, which concerns just Tehran city customers and its sample includes only individual customers, thus cannot be for institutional customers. Offering a case study, this paper presents a guide for banking system to predict any customer credit risk and regulate any customer loan in the light of customer risk that was extracted by neural network, and logistic regression employed different scientific methodologies in their service quality development efforts. Intending to offer scientific approaches to risk evaluation as a tool of customer credit risk assessment in banking system loan allocation procedures, this paper tries to bridge the current gap between academicians and practitioners; adds to the relatively limited theoretical literature.
Key words: Credit allocation, neural network, multilayer perceptron, logistic regression.
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