This document summarizes Stripe's approach to fraud detection using machine learning. Stripe builds customized random forests to detect fraudulent transactions and pseudorandom patterns in fraudster behavior. It evaluates models using counterfactual offline evaluation, where randomizing model decisions allows estimating performance without live experiments. This approach helps Stripe balance fraud prevention with merchant experience.