Provide an optimal model for determining and ranking inefficiency factors in the banking industry by combining data envelopment analysis and neural network

The purpose of this study is to combine two methods of data envelopment analysis and neural network in order to provide an optimal model for ranking inefficiency factors in the Iranian banking industry. First, through the study of theoretical foundations and interviews with banking experts, efficiency evaluation indicators in the banking industry were identified and finalized. In order to evaluate the efficiency of the units in the statistical population of the study, data envelopment analysis technique was used, especially the modified goal programming data envelopment analysis model, which was identified from 32 managements, 3 efficient managements and 29 inefficient managements. Then, the branches of inefficient management were evaluated and using the information of inefficient branches, the neural network matrix was prepared to identify the causes of inefficiency and the results were analyzed with different neural network models. The model with the lowest mean square error will be selected as the optimal model to determine the inefficiency factors. As a result, the self-organized mapping model with hyperbolic tangent transfer function and 0.9 momentum training rule was selected. By analyzing the sensitivity of this method, the indicators of provincial liquidity share, personnel distribution and operating costs were selected as the most important factors of inefficiency.