Evaluating and Improving Attrition Models for the Retail Banking Industry

Abstract

Analyzing customer attrition in the retail banking industry allows banks to quantify the likelihood of a customer closing their account. With the onset of online banking services, it has become important to both understand the latent behavioral patterns behind attrition and predict the event of attrition well before losing a customer. Presently, attrition models measure hard attrition, the event of a customer closing their account. By introducing a new latent probabilistic response variable, soft attrition, we aim to identify customers that tend towards attrition, which (i) increases the comprehensiveness of the customer base that is likely to churn, (ii) improves capability of predicting attrition events early, and (iii) helps identify key features associated with attrition. This paper introduces and evaluates methods that help redefine the attrition response variable and proposes techniques that improve on the existing attrition models, specifically in the retail banking industry.