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The banking industry plays a major role in the economic growth and development of a country. Thus, the assessment of bank performance is highly competitive for bank managers. Data Envelopment Analysis (DEA) is a pioneering approach in the literature for the assessment of organizations’ efficiency. Even so, the evaluation of DEA models is based on previously existing data, and they are unable to forecast future efficiency. To cover the shortcoming that occurs with DEA, this paper introduces a complementary method that integrates DEA as a powerful performance measurement approach and Artificial Neural Network (ANN) as a robust prediction system behavior. The Cerebellar Model Articulation Controller (CMAC) is a neural network that resembles a part of the brain known as cerebellum. The CMAC network, with its simple structure, is capable of estimating nonlinear functions, system modelling and pattern recognition. Meanwhile, the CMAC approach has fast learning convergence and local generalization compared to other networks. A set of 210 Iranian bank branches is involved in implementing the proposed method. The related historical data covers the period from April 2022 to March 2025 and consists of actual figures used to predict the' bank's future efficiency from April 2025 to March 2028. The empirical results revealed that the predicted efficiency using the DEA and CMAC network has a good correlation with that calculated by the DEA, indicating that the predicted efficiency using the proposed approach is a reasonable proxy to demonstrate efficiency for the future.

Keywords: Data Envelopment Analysis, Neural Networks, Cerebellar Model Articulation Controller, Efficiency, Bank Branch.

     

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