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Tuesday, June 25 • 10:00 - 10:20
Lessons Learned from Building a Credit Card Fraud Model

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This talk covers two ML problems Stripe faced (and solved!) in building its credit card fraud detection system:

- Addressing class imbalance in training data: legitimate transactions greatly outnumber fraudulent ones, and models trained on imbalanced data often have low precision
- Performing counterfactual evaluation to measure performance in production and obtain unbiased training data when the ML system itself is constantly changing outcomes (if we block a transaction we think is fraudulent, we don’t actually know if we got it right—we have no way to observe the transaction’s ultimate outcome)

avatar for Leela Senthil Nathan

Leela Senthil Nathan

Software Engineer, Stripe
Leela is a software engineer at Stripe, a technology company that builds economic infrastructure for the internet. She has spent two years working on various improvements to Stripe's transaction fraud models, including feature engineering, improvements to the training data generation... Read More →

Tuesday June 25, 2019 10:00 - 10:20 GMT-03
Room 8 Av. Rebouças, 3970 - Pinheiros, São Paulo - SP, 05402-600, Brazil