How I Built a Real-Time Fraud Detection System That Handles 71,000 RPS at p95 <6ms

Jun 3, 2026 · 2:17 AM UTC ·5 min read · 0 reactions · 0 comments · 38 views

#machinelearning #frauddetection #programming #systemsengineering

TL;DR · WeSearch summary

The article discusses the development of Sentinel, a real-time fraud detection system capable of processing 71,000 requests per second with a response time of under 6 milliseconds. It highlights the challenges of fraud detection, including the need for real-time classification and high accuracy. The author shares insights on using XGBoost and ONNX for model training and inference, emphasizing the performance benefits of implementing the system in Go.

Key facts

▪Sentinel processes 7.8 million requests with zero errors using machine learning techniques.
▪The system was designed to handle high throughput while maintaining accuracy and minimizing downtime.
▪The author trained the model on a heavily imbalanced dataset and achieved a PR-AUC of 0.87.

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Original publisher	DEV.to (Top)
Canonical URL	https://dev.to/sameer_ahmed_/how-i-built-a-real-time-fraud-detection-system-that-handles-71000-rps-at-p95-6ms-205k
Publication time	Wed, 03 Jun 2026 02:17:08 +0000
Retrieval time	2026-06-03T02:41:48.967Z
Last seen	2026-06-03T02:41:48.967Z
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Indexing	May the item be indexed (stored, ranked, made findable)?	Allowed
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Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.

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Opening excerpt (first ~120 words) tap to expand

try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3965551) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Sameer Ahmed Posted on Jun 3 How I Built a Real-Time Fraud Detection System That Handles 71,000 RPS at p95 <6ms #go #distributedsystems #machinelearning #programming 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.

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