Using AI and Behavioral Finance to Cope with Limited Attention and Reduce Overdraft Fees

Abstract

We test how effective a human-algorithm interaction is at stopping users from overdrawing their bank accounts. We use a randomized field experiment and draw our sample from users of a large personal financial management platform operating in the United States and Canada. We find that sending as-needed reminders is effective in and of itself, and the impact is intensified by the human response to the structure of the message. More simple messages are more effective, and the framing of the simplified message makes a difference. Users with medium to high annual incomes and users with fair to good credit scores are most likely to respond positively. We find that the investigated AI solution reduces information-gathering costs and has a positive effect but is not sufficient in all cases. Those with challenging financial situations may find it harder to act upon the warning. For our analysis we employ parametric identifications and time-to-event semi-parametric analysis. Our work contributes to the literature on financial technology as advisors, human-computer interaction, limited attention, behavioral finance, and experimental finance.

Keywords: artificial intelligence, behavioral finance, overdraft, limited attention, ostrich, Human algo interaction