Supporting Better Practice Benchmarking: A DEA-ANN Approach to Bank Branch Performance Assessment

Ioannis Tsolas*, Vincent Charles, Tatiana Gherman

*Corresponding author for this work

Research output: Contribution to JournalArticlepeer-review


The quest for best practices may lead to an increased risk of poor decision-making, especially when aiming to attain best practice levels reveals that efforts are beyond the organization’s present capabilities. This situation is commonly known as the “best practice trap”. Motivated by such observation, the purpose of the present paper is to develop a practical methodology to support better practice benchmarking, with an application to the banking sector. In this sense, we develop a two-stage hybrid model that employs Artificial Neural Network (ANN) via integration with Data Envelopment Analysis (DEA), which is used as a preprocessor, to investigate the ability of the DEA-ANN approach to classify the sampled branches of a Greek bank into predefined efficiency classes. ANN is integrated with a family of radial and non-radial DEA models. This combined approach effectively captures the information contained in the characteristics of the sampled branches, and subsequently demonstrates a satisfactory classification ability especially for the efficient branches. Our prediction results are presented using four performance measures (hit rates): percent success rate of classifying a bank branch’s performance exactly or within one class of its actual performance, as well as just one class above the actual class and just one class below the actual class. The proposed modeling approach integrates the DEA context with ANN and advances benchmarking practices to enhance the decision-making process for efficiency improvement.
Original languageEnglish
Pages (from-to)1-12
Number of pages12
JournalExpert Systems with Applications
Early online date30 Jun 2020
Publication statusE-pub ahead of print - 30 Jun 2020


  • Artificial Neural Network
  • Data Envelopment Analysis
  • Banking
  • Performance
  • Best Practice
  • Benchmarking
  • Artificial Intelligence


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