Bank Efficiency Forecasting Model Based on the Modern Banking Indicators Using a Hybrid Approach of Dynamic Stochastic DEA and Meta-Heuristic Algorithms

Evaluating the efficiency of banks is crucial to orient their future decisions. In this regard, this paper proposes a new model based on dynamic stochastic data envelopment analysis in a fuzzy environment by considering the modern banking indicators to predict the efficiency of banks, which belongs to the category of NP-hard problems. To deal with the uncertainty in efficiency forecasting, the mean chance theory was used to express the constraints of the model and the expected value in its objective function to forecast the expected efficiency of banks. To solve the proposed model, two hybrid algorithms were designed by combining Monte Carlo (MC) simulation technique with Genetic Algorithm (GA) and Imperialist Competitive Algorithm (ICA). In order to improve the performances of MC-GA and MC-ICA parameters, the Response Surface Methodology (RSM) was applied to set their proper values. In addition, a case study in the modern banking industry was presented to evaluate the performance of the proposed model and the effectiveness of the hybrid algorithms. The results showed that the proposed model had high accuracy in predicting efficiency. Finally, to validate the designed hybrid algorithms, their results were compared with each other in terms of accuracy and convergence speed to the solution.