How I Built a Real-Time Fraud Detection System That Handles 71,000 RPS at p95 <6ms A...
Sentinel processes 71,000 RPS at p95 <6ms with XGBoost/ONNX in Go, achieving 99% cache hit rate and zero errors on 7.8M transactions. Atomic model updates and A/B rollout eliminate downtime—essential architecture patterns for payment fraud systems where inference latency and detection accuracy directly impact revenue.
How I Built a Real-Time Fraud Detection System That Handles 71,000 RPS at p95 <6ms
A deep dive into building Sentinel — an ML inference pipeline that processes 7.8M requests with zero errors, using XGBoost, ONNX, and Go.
The Problem
Fraud detection is a classic hard problem in systems design. You need to:
Classify transactions in real-time — users can't wait 100ms for a payment to go through
Fraud detection is a latency race you don't control. Learn how feature stores, sliding windows, and Redis keep scoring fast at…
There is a number that haunts every fraud detection engineer: 0.13%. That is the fraud rate in the...
AI is transforming both the financial fraud attack surface and the defensive playbook.
Late at night, while most people scroll through social media, millions of unseen packets travel...
How Memory Changed the Behavior of My Fraud Investigation Agent ...
Building a High-Performance Real-Time Data Pipeline with Edge Inference and...
