Case Study
Pulse FinTech
Real-time fraud detection system protecting 8M+ daily transactions.
FinTech
3.2x fraud catch rate
Outcome
$16.7M fraud prevented
Quantified Impact
Challenge
Manual review caused false positives that frustrated customers and slowed approval.
Strategy
Deployed a graph-based fraud detection model with explainability.
Execution
- • Built transaction knowledge graph tracking user entities and patterns.
- • Trained GNN-based fraud classifier on 18 months of labeled data.
- • Integrated with payment processor for sub-100ms decision latency.
Tech stack
DGLPyTorchArangoDBgRPCNext.js
Results
- • 3.2x increase in caught fraud
- • 2.1% false positive rate (vs 8% baseline)
- • Reduced customer friction on approvals
Testimonial
"The graph approach caught sophisticated fraud patterns we never saw before."
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