Stripe builds a payments foundation model lifting card-testing detection from 59% to 97% and scales internal AI to 8,500 employees daily
Traditional task-specific ML models achieved only 59% accuracy at detecting card-testing attacks hidden inside large-merchant transaction volumes. As AI inference costs rose, free trial and refund abuse became existentially threatening to AI businesses. Internally, Stripe's data discovery was hampered by low-quality tables and lack of persona context for table selection.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Transaction enters charge path
Every Stripe transaction enters the charge path, with the platform processing approximately 50,000 transactions per minute.
The payments foundation model lifted large-merchant card-testing detection from 59% to 97%, processing every Stripe transaction in under 100ms. Internal AI adoption reached approximately 8,500 employees daily, with 65–70% of engineers using AI coding assistants. An LLM-assisted pan-EU payment integration was completed in approximately two weeks rather than approximately two months.
What failed first
Task-specific ML models trained on fraudulent dispute labels achieved only 59% accuracy at detecting card-testing attacks hidden in large-merchant transaction volumes.