Real-Time Fraud Detection
Stopping financial fraud in under 50ms with ensemble ML models
Nexus Financial processes $2.3B in transactions daily. Their rule-based fraud system was flagging too many legitimate transactions while missing sophisticated fraud patterns. We built a real-time ML pipeline that evaluates every transaction in under 50ms with 99.4% accuracy — reducing false positives by 67% and catching $14M in fraud in the first quarter.
What they faced
Nexus Financial's existing fraud detection relied on a system of 2,000+ hand-written rules accumulated over 15 years. The rules were effective against known fraud patterns but brittle against novel attacks. False positive rates had climbed to 8.3%, meaning 1 in 12 legitimate transactions was being flagged for manual review — creating a terrible customer experience and costing $3.2M annually in review staff. Meanwhile, sophisticated fraud rings using synthetic identities and coordinated small transactions were slipping through the rule-based net. Nexus needed a system that could adapt in real-time.
What we built
We designed a three-layer fraud detection architecture. The first layer uses lightweight feature engineering to score transactions in under 10ms using historical patterns. The second layer applies an ensemble of gradient-boosted trees and neural networks for deeper behavioral analysis. The third layer uses graph neural networks to detect coordinated fraud rings by analyzing transaction networks. All three layers run in parallel and their scores are combined by a meta-model that makes the final block/allow decision in under 50ms total.
How we built it
Real-Time Feature Store
Built a feature store computing 400+ features per transaction in real-time: velocity checks, geographic patterns, device fingerprints, merchant risk scores, and behavioral biometrics. Features are computed in under 5ms using a custom Redis-backed pipeline.
Ensemble Scoring Engine
Deployed an ensemble of XGBoost models (fast, interpretable) and deep neural networks (complex pattern detection) running in parallel. Models are specialized: one for card-not-present fraud, another for account takeover, a third for synthetic identity fraud.
Graph-Based Ring Detection
Implemented a graph neural network that maps relationships between accounts, devices, merchants, and IP addresses. This layer detects coordinated fraud rings that appear normal at the individual transaction level but form suspicious patterns when viewed as a network.
Adaptive Learning & A/B Testing
Models retrain daily on confirmed fraud outcomes. New model versions are deployed via shadow scoring and A/B testing to ensure improvements before full rollout. A drift detection system alerts the team when transaction patterns shift significantly.
Impact delivered
- ✓ Transaction scoring latency under 50ms at p99, handling 26,000 transactions per second
- ✓ Fraud detection accuracy of 99.4%, up from 94.1% with the rule-based system
- ✓ False positive rate reduced from 8.3% to 2.7%, saving $2.1M annually in review costs
- ✓ $14M in fraud caught in the first quarter that would have been missed by the old system
- ✓ Detected and dismantled 3 coordinated fraud rings using the graph analysis layer
"The difference was immediate. Our fraud losses dropped, our customers stopped complaining about blocked transactions, and we dismantled fraud rings we didn't even know existed. This system pays for itself every single month."
Technologies used
Ready to build something like this?
Let's discuss how AI can transform your business.
Start a Conversation