Cutting fraud losses 63% with a real-time AI detection engine
We built a real-time transaction risk-scoring engine that flags fraudulent activity in under 80ms, replacing a rules-only system with an adaptive ML pipeline.
63%
Reduction in confirmed fraud losses
41%
Fewer false-positive declines
78ms
Median scoring latency
6 weeks
From kickoff to production
Transaction TXN-88215
Risk factors
Model confidence
76 / 100
Analyst review queue
Fraud trends
Confirmed fraud, last 6 weeks
The challenge
Our client's legacy rules-based fraud engine generated high false-positive rates, frustrating legitimate customers while still missing sophisticated fraud patterns. Their team needed a system that could learn from new fraud patterns without a multi-week rule-deployment cycle, and that could score transactions within their existing sub-100ms authorization window.
Our solution
We designed a hybrid architecture combining gradient-boosted risk models with a real-time feature store, deployed behind a low-latency inference API. A human-in-the-loop review queue routes borderline cases to fraud analysts, and every decision feeds back into weekly model retraining. The system integrates directly into the client's existing authorization pipeline with zero downtime during rollout.
Let's talk about your project
Every engagement starts with a scoping conversation, not a sales pitch. Tell us what you're building.