Decision making on allocation of limited financial resources and determining the service quality level and facilities to the customers is an important issue for banking industries and financial enterprises. In this research by using neural networks, customer’s credibly behavior is modeled and clustered in order to optimize the allocation of financial resources and enhance the quality of banking services. By using Analytic Hierarchy Process (AHP), weighting coefficients of each input variable is determined and then these coefficients are used as primarily weights in fuzzy neural networks (FNN). This approach has increased training speed and accuracy of FNN considerably. Also the customers credibly behavior is predicted by using neural networks clustering models. The fuzzy neural networks model with the same data is also carried out without using AHP weights. The comparison of both approaches show the fuzzy neural networks clustering models with AHP weightings is more accurate with higher prediction speed. The model is implemented for a real application of Iranian National Bank. The case study shows 98% increase in prediction speed and 12% increase in accuracy.
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