What is Rules Based Fraud Detection?

Rules‑based fraud detection utilizes human‑written conditions to identify blatant symptoms: excessive attempts, BIN/country mismatch, first‑time payee over limit, repeated declines, proxy ASNs. It is transparent and lightning fast to deploy. It also goes stale if you don’t prune, test, and measure.

Make rules work: reason code every block, simulate ahead of launch, and monitor precision/recall – not just “blocks”. Keep distinct profiles by product and season – holiday traffic isn’t Tuesday traffic. Feed confirmed outcomes back into tuning and retire rules that only cost conversion.

Pass the gray to models and targeted step‑ups. When identity is in question, route users through lightning fast identity verification. At checkout, a layered payment fraud prevention strategy can orchestrate rules and ML together so friction lands precisely where it should.

Simple, visible, effective – if you keep it honest.