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

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 artificial intelligence 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 semiparametric analysis. Our work contributes to the literature on financial technology as advisors, human–computer interaction, limited attention, behavioral finance, and experimental finance. This paper was accepted by Jean-Edouard Colliard, Special Issue on the Human-Algorithm Connection. Funding: O. Sade acknowledges financial support for this research from the Krueger Center and the Albertson-Waltuch Chair in Business Administration. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.00304 .