Fulton Bank, worked with the Digital Innovation Foundry (DIF) to develop a data-driven strategy for identifying prospective mass affluent customers. The DIF team, comprising Wei Wang (Associate Professor of Accounting), Sudipta Basu (Professor of Accounting), and Manoj Chacko, brings academic expertise to inform and guide the project.
This project analyzes how client age relates to deposit and loan balances at Fulton Bank using the bank’s proprietary, anonymized client data, combined with publicly available ZIP-code-level measures of neighborhood wealth and demographics. Using various statistical techniques including scatterplot. locally estimated scatterplot smoothing (LOESS), and regression analyses, we identify distinct life-cycle patterns in client behavior. Specifically, deposit balances increase steadily with age and rise disproportionately once clients enter their 50s, and this effect is more pronounced in ZIP codes with higher home values. Meanwhile, loan balances follow a hump-shaped pattern, peaking among clients in their 30s and declining thereafter. The patterns remain robust after controlling for neighborhood characteristics and state-level differences, indicating they reflect true life-cycle behavior rather than confounding geographical differences. Overall, the findings highlight distinct opportunities for the bank to expand lending relationships with existing and prospective clients.
