An integrated data envelopment analysis–artificial neural network approach for benchmarking of bank branches

Elsa Shokrollahpour,Mostafa Zandieh,Farhad Hosseinzadeh Lotfi

doi:10.1007/s40092-015-0125-7

Abstract

Efficiency and quality of services are crucial to today’s banking industries. The competition in this section has become increasingly intense, as a result of fast improvements in Technology. Therefore, performance analysis of the banking sectors attracts more attention these days. Even though data envelopment analysis (DEA) is a pioneer approach in the literature as of an efficiency measurement tool and finding benchmarks, it is on the other hand unable to demonstrate the possible future benchmarks. The drawback to it could be that the benchmarks it provides us with, may still be less efficient compared to the more advanced future benchmarks. To cover for this weakness, artificial neural network is integrated with DEA in this paper to calculate the relative efficiency and more reliable benchmarks of one of the Iranian commercial bank branches. Therefore, each branch could have a strategy to improve the efficiency and eliminate the cause of inefficiencies based on a 5-year time forecast.

Highlights

Since banking industry is highly competitive, the performance assessment has been receiving more attention recently
Even though data envelopment analysis (DEA) is a pioneer approach in the literature as of an efficiency measurement tool and finding benchmarks, it is on the other hand unable to demonstrate the possible future benchmarks
To cover for this weakness, artificial neural network is integrated with DEA in this paper to calculate the relative efficiency and more reliable benchmarks of one of the Iranian commercial bank branches

Summary

Introduction

Since banking industry is highly competitive, the performance assessment has been receiving more attention recently. As DEA can hardly predict the performance of other decision-making units, Wang (2003) used artificial neural network (ANN) to assist in estimating efficiency. Wu et al (2006) combined DEA and ANN for measuring the performance of a large Canadian bank They came to the conclusion that the DEA–ANN method produces a more robust frontier and helps to identify more efficient units. For inefficient units, it provides the guidance on how to improve their performance to different efficiency ratings They concluded there was no need to make assumptions according to the production function in the ANN approach (the major drawback of the parametric approach) and that it is highly flexible, and that the weakness of the DEA in forecasting is the reason to use ANN (Wu et al 2006).

Ray unboundedness

Display the frontier in a specific time horizon

Result

Pm m 1

Conclusions and future works