Payment Processors

AML compliance for payment processors

High-volume transaction monitoring built for multi-scheme payment environments. FCA PSR-compliant monitoring that scales with your transaction volume — not your headcount.

The Challenge

Three pressures every payments MLRO recognises

Transaction Volume Scaling

Alert queues grow with transaction volume. Legacy rules calibrated for smaller book sizes produce proportionally more false positives as volumes increase.

Multi-Scheme Complexity

Visa, Mastercard, Faster Payments, BACS, CHAPS — each scheme has different fraud and AML typologies. Generic rule sets miss scheme-specific patterns.

FCA Regulatory Reporting

PSRs 2017 and MLR 2017 impose specific SAR filing and record-keeping obligations. Manual processes create filing timeline risk and audit trail gaps.

How RegSynq Fits

Built to address payment processor compliance at scale

Volume-Scalable Monitoring

Sub-50ms evaluation per transaction — no degradation at 1M+ transactions/day. Horizontal scale without compliance team headcount increase.

Scheme-Agnostic Rule Sets

Typologies built for payment schemes — Faster Payments velocity patterns, CHAPS high-value wire monitoring, card scheme fraud indicators all covered.

Automated SAR Filing

From flag to NCA submission within 2 hours. No manual drafting, no timeline risk. Complete filing evidence pack generated automatically.

FCA Audit Trail

Immutable decision log for every alert — suppress, escalate, file. FCA supervisory teams can access a complete compliance evidence pack at any inspection.

The volume we process was breaking our legacy monitoring setup — we were generating 2,800 alerts a day with a three-person compliance team. RegSynq cut that to around 90 actionable alerts. Our MLRO can actually spend time on investigation now.

F
Head of Financial Crime
MLRO at a challenger payments platform

High-volume payment monitoring, calibrated for your scheme and risk profile.

Request trial access and our compliance team will configure RegSynq for your transaction profile.