Real-time fraud detection for a US payment platform
94%
Fraud detection rate
73%
Reduction in false positives
<50ms
Scoring latency
$1.8M
Annual savings
The challenge
A fast-growing payment platform was losing $2M+ annually to fraudulent transactions. Their rule-based system caught less than 40% of fraud and generated excessive false positives that blocked legitimate customers.
Our solution
We built a real-time ML pipeline that ingests transaction data, extracts 200+ features, and scores every transaction in under 50ms. The system combines gradient-boosted models with a graph neural network that detects coordinated fraud rings. We deployed it with full MLOps: automated retraining, A/B testing, model monitoring, and drift detection.
Key highlights
Feature extraction
200+ transaction features computed in real time
Graph neural network
Detects coordinated fraud rings across accounts
MLOps pipeline
Automated retraining, A/B testing, drift detection
Sub-50ms scoring
Real-time decision on every transaction
Tech stack
“JustSoftLab didn't just build a model — they built a production system that our team can maintain and improve.”
VP of Engineering
US Payment Platform
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