Optimizing Bank Overdraft Fees with Big Data

In 2012, consumers paid $32 billion in overdraft fees, representing the single largest source of revenue for banks from demand deposit accounts during this period. Owing to consumer attrition caused by overdraft fees and potential government regulations to reform these fees, financial institutions have become motivated to investigate their overdraft fee structures. Banks need to balance the revenue generated from overdraft fees with consumer dissatisfaction and potential churn caused by these fees. However, no empirical research has been conducted to explain consumer responses to overdraft fees or to evaluate alternative pricing and product strategies associated with these fees. In this research, we propose a dynamic structural model with consumer monitoring costs and dissatisfaction associated with overdraft fees. We find that consumers heavily discount the future and potentially overdraw because of impulsive spending. However, we also find that high monitoring costs hinder consumers’ effort to track their balance accurately; consequently, consumers may overdraw because of rational inattention. We apply the model to an enterprise-level dataset of more than 500,000 accounts with a history of 450 days, providing a total of 200 million transactions. This large dataset is necessary because of the infrequent nature of overdrafts; however, it also engenders computational challenges, which we address by using parallel computing techniques. Our policy simulations show that alternative pricing strategies may increase bank revenue and improve consumer welfare.