MSAMonitoring and Surveillance Activities
The techniques and tools a financial institution uses to monitor transactions and customer behavior and to detect unusual or suspicious activity. Monitoring and surveillance activities combine rule-based scenarios, AI and machine learning models, and analysis of typologies to surface hidden risks.
Why it matters
Regulators expect monitoring to match an institution's risk profile: its products, customers, channels and geographies. Monitoring that is too narrow misses suspicious activity, while monitoring that is too broad buries investigators in false positives and delays the cases that matter.
Most programs layer several methods. Rules and thresholds catch known patterns, such as structuring. Behavioral models compare activity against a customer's own history and peer group. Network analysis links accounts that act together. Alerts then flow to investigators, who decide whether to close, escalate or file a suspicious activity report.
The main change in recent years is the shift from static rules toward models that learn normal behavior and flag deviations, including unsupervised machine learning, which can detect new typologies without labeled examples. ThetaRay applies this approach to transaction monitoring so teams can find unknown risks while reducing alert volumes.
Protect the flow. Power the growth.
See how ThetaRay turns regulatory expectations into detection and investigation that hold up to scrutiny.