Scaling fraud detection from 5K to 50K transactions per second
How we built a real-time fraud detection engine that processes 50K transactions per second with sub-50ms latency.
When PayFlow came to us, they were processing 5,000 transactions per second with a legacy fraud system that was missing sophisticated attacks. They needed to scale to 50K TPS without adding latency.
The challenge: fraud detection is inherently a real-time problem. You have milliseconds to decide whether a transaction is legitimate. Batch processing doesn't work.
Our approach:
**1. Feature engineering at the edge.** We pre-compute features (velocity, amount patterns, device fingerprints) at the API gateway, before the transaction even hits the ML model.
**2. Lightweight models.** We use gradient-boosted trees (XGBoost) instead of deep neural networks. They're 10x faster for inference and just as accurate for tabular transaction data.
**3. Streaming architecture.** Transactions flow through a Kafka stream. The fraud model scores each transaction in parallel. Decisions are made in under 50ms.
**4. Continuous learning.** The model retrains nightly on confirmed fraud cases. New attack patterns are caught within 24 hours.
The result: 99.2% fraud detection accuracy, 0.3% false positive rate, and sub-50ms latency at 50K TPS.