Evaluating the efficiency of the commercial banks admired in Fortune 500 list; Using data envelopment analysis

Abstract

The aim of this paper is to propose a new approach for identifying the relatively most efficient banks in future 500 list. The second purpose of this paper is to suggest benchmarks for the banks that were not considered efficient. In this paper, data envelopment analysis (DEA) was used to discriminate efficient banks from the inefficient ones. To do so, each bank was considered as a decision making unit (DMU). Then, experts assigned inputs and outputs to the model. They suggested assets as the input and revenue, profit, and total stockholders' equity as the output. Having discriminated the DMUs, we ranked them and ran a sensitivity analysis on the data. Necessary data were acquired from the Fortune 500 list. The results, findings, implications, and suggestions are presented in the paper. DEA results can serve as a benchmark for the inefficient DMUs. That is, each inefficient bank can find its benchmark and set proper policies to achieve the efficiency with least wastage of budget and time. The findings reveal that the banks with higher asset and profit are not necessarily the most efficient.