In a controlled comparison, the new configuration improved recall by over 8%, translating to an estimated tens of millions of dollars a year in additional fraud prevention for Stripe’s merchants.
StripePublished by the organization itself (post), no named author
Post: Scaling Radar: Fighting fraud with agent-assisted machine learning, Stripe developer blog, October 19, 2026, at Learning from a fraud attack.
Checked against the source by a first reader, then confirmed independently by a second reader who fetched the source without seeing the first reader's notes (Independent review agent (Claude Sonnet), blind second pass from passage text and citation only, no repository access, 2026-11-08).
Exact match under 'Learning from a fraud attack', including 'Stripe’s merchants'.
Stripe, “Scaling Radar: Fighting fraud with agent-assisted machine learning”, Stripe developer blog, 2026-10-19, at Learning from a fraud attack. https://stripe.dev/blog/scaling-radar-fighting-fraud-with-agent-assisted-machine-learning Archived at https://the-ai-record.com/passages/stripe-controlled-comparison-new-configuration-improved-recall-2026
Agents work across the experiment lifecycle. Engineers review hypotheses and results and decide what to deploy.
StripePublished by the organization itself (post), no named author
Post: Scaling Radar: Fighting fraud with agent-assisted machine learning, Stripe developer blog, October 19, 2026, at Giving agents the experiment lifecycle.
We built Agentic MLE, short for machine learning engineer, to take on more of that work. It’s a set of tools and instructions that lets AI agents operate in Stripe's existing ML infrastructure, carrying an experiment from an idea to a result: explore the data, implement a feature, compute its historical values, train a model, and evaluate it against a baseline.
StripePublished by the organization itself (post), no named author
Post: Scaling Radar: Fighting fraud with agent-assisted machine learning, Stripe developer blog, October 19, 2026, at Giving agents the experiment lifecycle.
Humans helped prioritize 38 ideas, then agents implemented the changes, trained models, debugged failed runs, and evaluated the results.
Ryan DrapeauMachine learning engineer, Intelligent Commerce · Practitioner, Stripe
Post: Stripe developer blog, Stripe developer blog, October 19, 2026.
We organized this knowledge into reusable procedures for the platform and model-specific documentation that an agent loads for the task at hand.
StripePublished by the organization itself (post), no named author
Post: Scaling Radar: Fighting fraud with agent-assisted machine learning, Stripe developer blog, October 19, 2026, at Giving agents the experiment lifecycle.
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