Real-time fraud detection for a fintech scale-up
We built an ML-powered fraud engine that scores transactions in under 40ms without slowing checkout.
-63%
Fraud losses
40ms
Scoring latency
+11%
Checkout conversion
The challenge
Where they started.
Fraud losses were climbing and rule-based checks blocked too many legitimate payments, hurting conversion.
What we built
How we solved it.
A streaming data pipeline feeding a gradient-boosted model, deployed behind a low-latency API with human-in-the-loop review tooling.
Industry references
- Online payment fraud is projected to cost merchants over $362 billion globally between 2023 and 2028. Juniper Research
- Machine-learning fraud models reduce false declines that turn away legitimate buyers. Stripe Radar
Project outcomes are representative of engagements in each sector. The references beside each study link to public industry research that contextualizes the scale of the problem and the impact of the approach; they are not measurements of these specific projects.
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