Fraud detection running at 99.95% precision
A multi-stage fraud detection system combining ML classifiers, LLM-based pattern analysis, and human-in-the-loop review for edge cases. Sub-100ms inline scoring.
The challenge
SentinelPay came to us with a clear problem: the existing system was slow, manual, and not scaling with growth.
Our approach
Two-week discovery sprint, four-week build with weekly production deploys, followed by four weeks of tuning and observability work.
The outcome
99.95% precision, 0.02% false-positive rate, $4.2M fraud prevented. The system now runs with minimal oversight and has headroom for 3× current load.
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