Bank Branch Grouping Strategy, an Unusual DEA Application

Abstract

This study uses Data Envelopment Analysis (DEA) to develop a grouping strategy for the bank branches of a large Canadian Bank. In order to benchmark their branches’ performance, the Bank first clusters the branches based on community type and population size—a not fully satisfactory approach. Hence, DEA was used to develop a grouping approach using an input oriented BCC production model to capture and analyze the aggregated effects of many complex processes. The model examines the relationship between staff and transaction activities. The peer references produced by the DEA model illustrate that the Bank’s current clustering methodology fails to compare some branches that are similar from an operational perspective; a flaw in the Bank’s current grouping approach. The new grouping strategy offers a fair and equitable set of benchmarking peers for every inefficient branch.