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# How Stripe Detects Fraudulent Transactions Within 100 ms

**[ByteByteGo](https://daily.dev/sources/bytebytego)** · 15 min read · 19 upvotes · 0 comments

## Summary

Stripe's Radar fraud detection system evaluates over 1,000 signals per transaction in under 100ms with 99.9% accuracy. The post covers the architectural evolution from a Wide & Deep ensemble (XGBoost + DNN) to a ResNeXt-inspired multi-branch DNN called Shield NeXt, which cut training time by 85%. Key topics include Stripe's network-scale data advantage (90% of cards seen across multiple merchants), embedding-based feature representations for geographic fraud transfer, the precision-recall tradeoff and how merchants can configure thresholds and custom rules, explainability tooling built around deep neural networks, and production challenges like real-time feature computation, per-merchant model validation, and counterfactual analysis for blocked transactions.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://blog.bytebytego.com/p/how-stripe-detects-fraudulent-transactions>

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---

Tags: [#machine-learning](https://daily.dev/tags/machine-learning), [#neural-networks](https://daily.dev/tags/neural-networks), [#stripe](https://daily.dev/tags/stripe), [#fraud-detection](https://daily.dev/tags/fraud-detection), [#real-time-systems](https://daily.dev/tags/real-time-systems)

[View this post on daily.dev](https://daily.dev/posts/how-stripe-detects-fraudulent-transactions-within-100-ms-wm7qvbw9v)

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