Providing bank branch ranking algorithm with fuzzy data, using a combination of two methods DEA and MCDM

Abstract

Financial and credit institutions need to evaluate and rank their subsidiaries to control and improve their performances. There are several methods to evaluate the performance of such branches. In order to take advantage of the strengths of each of these methods and cover some of the limitations that exist in each of these methods alone, in this study, an algorithm which is a combination of multi-criteria decision-making methods, statistical analysis, and data envelopment analysis is proposed. The location of each of the methods mentioned in the steps of the algorithm, and its simulation to a standard linear programming model in MATLAB software, is the main research problem that is designed and presented for fuzzy type uncertain data. The proposed algorithm was used for 1736 branches of a certain bank in banking sector of Iran with uncertain data. Analysis of the results for different alpha-cuts and testing them with SPSS software show that with increasing the range of fuzzy numbers, the number of efficient branches increases and also affect the ranking. Nevertheless, there is still a significant correlation even in the alpha-cut changes in the ranking results.