When a customer overdraws their 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 can be generalized to provide ML-driven personalized financial advice for many different personal finance goals--increase credit score, build emergency savings fund, pay down debut, allocate capital for investment.

翻译：当客户账户透支导致余额为负时，需支付透支费用。美国人每年因不必要的透支费用支付约150亿美元，通常以35美元为单位递增；其中Mint个人理财应用用户每年支付的费用尤为显著，约为2.5亿美元。这些透支费用构成沉重的财务负担，且会引发连环透支收费，使客户陷入财务困境。为解决这一问题，我们构建了基于机器学习的透支预警系统（ODEWS），该系统利用Mint应用中的银行与交易数据，评估客户未来一周内发生透支的风险。系统向高风险客户发送警报，助其采取措施规避费用，最终改变其行为与财务习惯。与对照组相比，部署该系统后Mint用户累计节省了300万美元透支费用。此外，本文提出的方法论可推广至多种个人理财目标（如提升信用评分、建立应急储蓄、偿还债务、配置投资资本），提供基于机器学习的个性化财务建议。