Fraud prevention that catches patterns a rules engine alone will miss.

Most fraud prevention still runs on rules engines built around known fraud patterns — flag transactions over a threshold, flag mismatched billing addresses, flag rapid repeat purchases. Fraud tactics evolve faster than most teams can update the rulebook, and a purely rules-based system either misses new patterns entirely or over-flags legitimate customers into a frustrating review queue.

Almanor AI combines real-time transaction monitoring with trained fraud analysts who review flagged and borderline cases directly. The AI layer identifies suspicious patterns as they emerge, not just the ones already codified into rules, while analysts handle the judgment calls a model shouldn't be making alone — genuinely ambiguous cases, high-value disputes, and pattern investigation across multiple accounts.

The operating model is designed to reduce false positives as much as catch real fraud: a legitimate customer wrongly flagged is a real cost too, in lost revenue and damaged trust, and it gets the same attention as a missed fraud case.

AI-driven monitoring that flags suspicious transaction patterns as they occur, calibrated against your historical data.

Trained fraud analysts reviewing flagged and borderline transactions, with clear escalation paths for high-value or complex cases.

Pattern detection for account takeover attempts — unusual login behaviour, rapid account changes, credential-stuffing signatures.

Investigation support for disputed transactions, building the documentation needed for chargeback response.

Ongoing tuning to reduce legitimate customers being wrongly flagged, treating false positives as a real cost to solve for.

Regular reporting on emerging fraud patterns and trends specific to your platform, not generic industry benchmarks.