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Fraud Engines: Rules vs Machine Learning

Payomatix RiskJune 19, 20256 min read

Rules: Strengths & Weaknesses

Rules (e.g. "decline if 5+ orders from same IP in 1 hour") are deterministic, explainable, and instantly tunable. They're also brittle — fraudsters adapt in days.

ML: Strengths & Weaknesses

Models capture interactions humans miss and adapt as fraud patterns shift. They require training data, monitoring, and explainability tooling.

The Right Architecture

  • Hard rules for known-bad signals (sanctioned country, known-bad device).
  • ML model for the marginal middle.
  • Manual review for the highest-risk borderline cases.
  • Feedback loop from confirmed fraud back into the model.
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    Hybrid engine, sub-50ms scoring, fully tunable rules layer, and explainability for every decision.

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