Transaction monitoring is the detection layer of an AML programme: watching transaction patterns in real time or near real time and generating alerts when something matches a defined typology. The engineering challenge is scale (monitoring millions of transactions) combined with precision (not burying analysts in noise).
We build monitoring engines around deterministic rules, since every alert needs an explainable reason for an examiner, with machine learning used to prioritise the analyst queue rather than replacing the rules that generate alerts.
What We Offer
Rule engine design
Deterministic detection rules built around your institution’s actual risk typologies, structured so a rule’s logic can be explained to an examiner in plain language.
Real-time and batch monitoring
Streaming detection for time-sensitive typologies and batch analysis for patterns that only become visible over a longer window, depending on what each typology actually requires.
ML-assisted prioritisation
A model that ranks the alert queue by likely relevance, improving analyst throughput without making an unexplainable model output the reason a case was opened or closed.
Threshold tuning and backtesting
Rules tested against historical transaction data before going live, so a new rule’s alert volume and accuracy are known quantities, not a surprise in production.
How We Help
The tension in every transaction monitoring build is between catching genuine suspicious activity and generating so many alerts that analysts cannot meaningfully review them. We resolve that with rules first, since a rule’s logic is auditable, and machine learning applied to prioritisation, since that keeps a human-explainable reason behind every alert of record.
Scale is the other real engineering problem: monitoring transactions in real time across a large institution is a streaming data problem as much as a compliance one, and the architecture has to handle both correctly.
Our Approach
New rules are backtested against historical data before deployment, so we know their alert volume and rough accuracy before analysts see a single live alert from them.
Machine learning sits on top of the rule engine as a prioritisation layer, never as the sole reason an alert exists, so every case an examiner reviews has a rule they can trace.
Technologies We Use
Industries We Support
Related case studies
- goAML-Integrated AML Monitoring for a Tier-1 Bank: Screening, monitoring and goAML reporting rebuilt around an immutable transaction event stream, so any alert can be reconstructed exactly as the system saw it.