Description
This talk covers three major ML problems Stripe faced (and solved!) in building its credit card fraud detection system: choosing labels for fraud that work across all merchants, addressing class imbalance (legitimate charges greatly outnumber fraudulent ones), and performing counterfactual evaluation (to measure performance and obtain training data when the ML system is changing outcomes itself).