Q: Rules-based fraud filters or machine-learning scoring — which should I rely on?

Q: Rules-based fraud filters or machine-learning scoring — which should I rely on?

A: They fail in opposite directions, which is exactly why mature setups run both.

Rule engines (if datacenter IP, then block; if 3+ conversions per minute, then flag) are transparent, instant, and easy to defend in a dispute. The weakness: they're static. Fraudsters probe until they learn your thresholds, then sit just under them.

Machine-learning scoring weighs dozens of signals and adapts to patterns no human wrote a rule for. The weakness: it's a black box. When it rejects a lead and the affiliate asks why, 'the model said so' is a bad answer — and it needs lots of clean labeled data to not drift.

How to split the work:
— Rules for the bright lines you can defend: legal blocks, declared bots, geo violations.
— ML for the fuzzy middle: 'this looks like a farm but no single rule trips.'
— Always keep rules as a backstop so a misbehaving model can't nuke good traffic overnight.

Short version: rules for explainable hard limits, ML for adaptive pattern-spotting. Rules protect you from the model; the model catches what rules can't.

Still stuck? Drop your case in the comments.