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All case studies

Finance · 2023

Real-time fraud detection at 40M-user scale

A real-time fraud platform that cut losses 34% while keeping false declines low and latency under 60ms.

A high-growth payments fintechFintech · 40M users

34%Reduction in fraud losses
<60msDecision latency p99
9%Reduction in false declines
100%Decisions explainable on demand

Client

A high-growth payments fintechFintech · 40M users

Technology stack

AWSKafkaFeature storePythonOnline inference

ROI

195% over 18 months, payback in 8 months.

Client overview

A payments fintech faced rising fraud losses and false-decline pressure as volume scaled. Its batch scoring could not keep pace with real-time attack patterns.

Business challenge

Build real-time, explainable fraud detection at scale, without inflating false declines or adding latency users would notice.

Approach

We designed a streaming fraud platform with feature stores, online and offline model training, and human-in-the-review loops. Explainability was built in so analysts and regulators could understand every block.

Business impact

Fraud losses fell while authorization rates rose. The platform's explainability became a trust asset in relationships with partners and regulators.

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