Stripe's ML flywheel cuts successful card-testing fraud attacks by 80%
StripeShepherd · FlyteStripe 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.
Overview
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.
The challenge
Card testing is one of the most significant fraud threats to Stripe, its users, and the broader financial ecosystem, and one of the most challenging to detect and block, because it blends in easily with legitimate traffic and bad actors are constantly changing their tactics. Unlike disputes or declines, card testing doesn't yield explicit labels that can be used to train models or evaluate prevalence or performance.
The solution
Stripe built an ML-based flywheel that applies models at three levels of abstraction — estimating overall card-testing prevalence, identifying where attacks are occurring, and scoring individual transactions — to dynamically set block thresholds. Labels are derived by consolidating intelligence on new attack vectors, automating discovery of hidden patterns from weaker signals, and manual expert review. New features are engineered on Stripe's Shepherd feature-engineering platform, built through a partnership with Airbnb, and tested and redeployed via the Flyte ML orchestration platform, including blue-green tests between old and new models. The flywheel is augmented by a large transformer model trained on billions of global transactions that generates embeddings used across multiple card-testing detection use cases.
Reported business value
Successful card-testing attacks on Stripe declined by 80% over the last two years, even as Stripe's payment volume expanded to over $1 trillion.
Sources
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