A Heuristic Framework for Assessing the Efficiency of Multi-branch Banks Under Big Data Conditions

Vahid Kayvanfar (), Hamed Baziyad, Shaya Sheikh and Frank Werner
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Vahid Kayvanfar: College of Science and Engineering, Hamad Bin Khalifa University
Hamed Baziyad: Tarbiat Modares University
Shaya Sheikh: New York Institute of Technology
Frank Werner: Otto-von-Guericke University Magdeburg

Chapter Chapter 22 in Business Analytics and Decision Making in Practice, 2024, pp 271-293 from Springer

Abstract: Abstract Evaluating the efficiency of organizations and branches within an organization is a challenging issue for managers. Evaluation criteria allow organizations to rank their internal units, identify their position concerning their competitors, and implement strategies for improvement and development purposes. Among the methods that have been applied in the evaluation of bank branches, non-parametric methods have captured the attention of researchers in recent years. One of the most widely used non-parametric methods is the data envelopment analysis (DEA) which leads to promising results. However, the static DEA approaches do not consider the time in the model. Therefore, this paper uses a dynamic DEA (DDEA) method to evaluate the branches of a private bank over three years (2017–2019). The results are then compared with static DEA. After ranking the branches, they are clustered using the K-means method. Finally, a comprehensive sensitivity analysis approach is introduced to help the managers to decide about changing variables to shift a branch from one cluster to a more efficient one.

Keywords: Dynamic data envelopment analysis (DDEA); Branch performance evaluation; Efficiency evaluation; Banking industry; Big data (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/978-3-031-61589-4_22

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