Write a high-performing fraud rule with me: A real-world example
Writing a fraud rule is easy, but writing a good one is not. In this post, Chen Zamir walks through a real-world example of building a high-performing fraud rule using an iterative, research-driven process. The blog explores how to identify inciting anomalies, explain false positives through behavioral context, apply trust signals, and balance precision and recall. Along the way, it highlights how modern tools, including LLMs and AI-assisted analysis, help fraud teams move faster, reduce noise, and build rules that are resilient to fraudster adaptation.
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