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FintechAtlas Fintech
Real-time fraud scoring with a custom ML pipeline
A custom machine-learning pipeline scores transactions in milliseconds, catching more fraud while approving more good payments.
31%More fraud caught
<50msScoring latency
0.4%False-positive rate
The challenge
Atlas relied on static rules that fraudsters learned to evade, while false positives blocked legitimate customers and drove support tickets.
What we built
We built a real-time machine-learning pipeline that scores every transaction in under fifty milliseconds, with continuous evaluation and a feedback loop from confirmed outcomes.
- Streaming feature computation at the edge of the request.
- Continuous evaluation against labelled outcomes.
- Explainable scores analysts can act on.
The results
Atlas caught 31% more fraud while cutting false positives to 0.4%, all within a 50ms scoring budget that kept checkout fast.