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.

翻译：当客户银行账户透支且余额为负时，银行会收取透支费用。美国人每年支付约150亿美元的非必要透支费用，通常以35美元为计费单位；其中使用Mint个人财务管理应用程序的用户每年支付的此类费用约为2.5亿美元。这些透支费用构成过重的财务负担，并可能引发连锁透支费用，使客户陷入财务困境。为解决此问题，我们开发了一套基于机器学习的透支预警系统（ODEWS），该系统通过分析客户在Mint应用程序中的银行账户及交易数据，评估其在未来一周内发生透支的风险。系统会向高风险客户发送预警通知，使其能够采取措施避免费用产生，最终改变其消费行为与财务习惯。实际部署数据显示，与对照组相比，该系统为Mint用户节省了300万美元的透支费用。此外，本文所述方法是构建机器学习驱动的个性化财务建议体系的重要环节，旨在帮助用户认知、增长并守护个人净资产，最终实现其财务目标。