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"Rules‑Based vs Machine Learning Fraud Protection: Which One Actually Works?"

Learn how modern fraud tools use machine learning to stay ahead--while rules alone are costing you conversions.

Traditional fraud systems rely on static rules: fixed filters, thresholds, and manually built decision logic. They’re easy to understand, but slow to change—and rigid in how they treat risk.

By contrast, machine learning‑driven protection adapts fast, uses thousands of signals, and helps distinguish fraud from legitimate behavior more accurately. The result? Fewer false declines, faster decisions, and a smoother checkout experience.

This guide will help you:

• See the limits of rule‑based fraud protection: high maintenance, blind spots, and risk of overblocking good customers
• Understand the advantages of machine learning: adaptability, scalability, and better precision in fraud detection
• Learn what features matter when choosing an ML‑powered solution: Transparency, explainability, data breadth

Get strategies to combine rules and ML in a way that balances risk, customer experience, and revenue.

Get the insights now.

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