Shepherd

Stripe's ML flywheel cuts successful card-testing fraud attacks by 80%

Stripe

Stripe built a machine learning-based system to detect and block card testing fraud, applying ML models at three levels of abstraction: overall prevalence estimation, identifying where attacks are occurring, and scoring individual transactions. A rapid data-labeling, retraining and redeployment pipeline, built on Stripe's Shepherd feature-engineering platform (developed with Airbnb) and the Flyte ML orchestration platform, lets the team react to new attack patterns within hours. The system is augmented by a large transformer model trained on billions of global transactions that generates embeddings used across card-testing detection use cases. Successful card-testing attacks on Stripe declined by 80% over two years even as Stripe's payment volume grew past $1 trillion.