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Authors: Avishek Kumar, Tyson Silver
KDD '24: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
Pages 5272 - 5282
Published: 24 August 2024 Publication History
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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.
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Teaser for KDD 2024 Mint Customers pay millions of dollars in Overdraft Fees each year. The authors use ML to provide an early warning to customers to prevent overdrafts and, ultimately, know, grow and protect their Net worth.
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Index Terms
Know, Grow, and Protect Net Worth: Using ML for Asset Protection by Preventing Overdraft Fees
Applied computing
Electronic commerce
Digital cash
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Published In
KDD '24: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
August 2024
6901 pages
ISBN:9798400704901
DOI:10.1145/3637528
- General Chairs:
- Ricardo Baeza-Yates
Northeastern University, USA
, - Francesco Bonchi
CENTAI / Eurecat, Italy
Copyright © 2024 ACM.
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Sponsors
- SIGMOD: ACM Special Interest Group on Management of Data
- SIGKDD: ACM Special Interest Group on Knowledge Discovery in Data
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 24 August 2024
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Author Tags
- early warning system
- neural networks
- overdrafts
- personal finance
Qualifiers
- Research-article
Conference
KDD '24
Sponsor:
- SIGMOD
- SIGKDD
KDD '24: The 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
August 25 - 29, 2024
Barcelona, Spain
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Overall Acceptance Rate 1,133 of 8,635 submissions, 13%
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