Bank branch operational performance: A robust multivariate and clustering approach

• Integration of robust clustering analysis and DEA to study bank branch performance.
• Detection of influential branches, i.e., exhibiting extreme operating behaviors.
• Detection of influential branches affecting the clustering and efficiency performance.
• Exploration of how peer selection is affected by influential branches.
• Influential-based and cluster profiles that inform network design decisions.

This paper proposes a multi-step procedure that integrates robust methods, clustering analysis and data envelopment analysis (DEA) to identify bank branch managerial clusters and to study efficiency performance. By applying robust techniques based on principal component analysis, we look for (1) the detection of influential branches, i.e., exhibiting extreme operating behaviors, and (2) the clustering of branches based on operating characteristics. Our premise is that influential branches affect both the clustering and the determination of efficiency performance. The application of the procedure yields various aggregate influential-based branch profiles along with cluster profiles. These aggregate profiles provide valuable insights on the determinants of branch efficiency performance and operating patterns. Using the profiles as contextual information, DEA input-oriented slack-based models are applied to study branch efficiency performance from meta-frontier and cluster-frontier perspectives. Branch performance is characterized in terms of influential-based and cluster profiles, and efficiency designations. This allows for the understanding of how efficiency and peer selection are affected by influential branches, and how the profiles can be used to inform design decisions.