Modeling bank performance: A novel fuzzy two-stage DEA approach

TL;DR

This paper introduces a new fuzzy two-stage DEA model based on a modified Enhanced Russell Model to evaluate bank branch performance, accounting for internal interactions and fuzzy variables, demonstrated on Melli bank branches.

Contribution

It presents a novel fuzzy two-stage DEA approach that incorporates internal process interactions and fuzzy data for bank performance evaluation.

Findings

Effective in measuring bank branch efficiency

Handles fuzzy input, output, and intermediate data

Provides insights into internal process performance

Abstract

Evaluating the banks' performance has always been of interest due to their crucial role in the economic development of each country. Data envelopment analysis (DEA) has been widely used for measuring the performance of bank branches. In the conventional DEA approach, decision making units (DMUs) are regarded as black boxes that transform sets of inputs into sets of outputs without considering the internal interactions taking place within each DMU. Two-stage DEA models are designed to overcome this shortfall. Thus, this paper presented a new two-stage DEA model based on a modification on Enhanced Russell Model. On the other hand, in many situations, such as in a manufacturing system, a production process or a service system, inputs, intermediates and outputs can be given as a fuzzy variable. The main aim of this paper is to build and present a new fuzzy two-stage DEA model for measuring the efficiency of 15 branches of Melli bank in Hamedan province.