Know, Grow, and Protect Net Worth: Using ML for Asset Protection by Preventing Overdraft Fees

TL;DR

This paper presents an ML-based early warning system that predicts overdraft risk within a week, helping users avoid fees and saving millions in overdraft costs, thereby promoting better financial habits.

Contribution

The paper introduces a novel ML-driven overdraft prediction system integrated into a personal finance app, significantly reducing overdraft fees for users.

Findings

Saved $3 million in overdraft fees for Mint users

Successfully predicted overdraft risk within a week

Enhanced user financial behavior through alerts

Abstract

When a customer overdraws their bank account and their balance is negative they are assessed an overdraft fee. Americans pay approximately \$15 billion in unnecessary overdraft fees a year, often in \$35 increments; users of the Mint personal finance app pay approximately \$250 million in fees a year in particular. These overdraft fees are an excessive financial burden and lead to cascading overdraft fees trapping customers in financial hardship. To address this problem, we have created an ML-driven overdraft early warning system (ODEWS) that assesses a customer's risk of overdrafting within the next week using their banking and transaction data in the Mint app. At-risk customers are sent an alert so they can take steps to avoid the fee, ultimately changing their behavior and financial habits. The system deployed resulted in a \$3 million savings in overdraft fees for Mint customers compared to a control group. Moreover, the methodology outlined here is part of a greater effort to provide ML-driven personalized financial advice to help our members know, grow, and protect their net worth, ultimately, achieving their financial goals.