Anomaly Detection
Anomaly Detection: At a Glance
- Anomaly or outlier detection involves monitoring customer behaviours to identify deviations from normal or expected behaviour and combat money laundering & terrorist financing.
- Regulators expect regulated entities to implement effective monitoring systems for pattern deviation detection and ensure AML/CFT compliance.
- Effective anomaly detection systems, such as Citadel365, with data-driven monitoring capabilities and behavioural analytics, identify unexpected activity from normal customer behaviours for early ML/TF risk detection.
What Is Anomaly Detection in AML/CFT
Anomaly detection means identifying the unusual transaction patterns or customer behaviours that do not align with the customer’s expected financial activity. It further includes detecting suspicious behaviour that indicates money laundering, terrorist financing, and other financial crimes.
Anomaly detection complements rule-based monitoring systems, which detect known, specific ML/TF risk typologies significantly above thresholds. Anomaly detection identifies unknown patterns that criminals use to bypass compliance checks.
How Anomaly Detection Works in AML Systems
AML systems analyse customer behaviour using their past records to establish a baseline behaviour, treating it as expected activity. Further, the AML systems identify patterns that deviate from the expected activity, such as unusual payment volumes, frequencies or use of unknown counterparties.
AML systems use methods such as comparing observed data with the expected patterns, learning underlying patterns to detect deviations, or defining specific rules to flag anomalies while monitoring. With this, the AML systems generate alerts when anomalies are detected, enabling compliance teams to conduct further investigations and submit STR/SAR reports.
Common Use Cases of Anomaly Detection in AML
- Unusual transaction patterns or sudden changes in customer behaviour, such as a sudden rise in transaction volume.
- Patterns such as frequent, large deposits and instant withdrawals or transfers that indicate money mule or fraud-related activity.
- Abnormal activity, such as cross-border transfers to high-risk jurisdictions, or access from a new country or device, can be monitored by monitoring high-risk customers or accounts.
- Layering or structuring patterns, such as multiple small transactions under thresholds.
Regulatory Expectations for Anomaly Detection and Monitoring
Regulatory authorities require regulated entities to implement effective monitoring systems that help detect structuring or layering patterns below thresholds, rather than just provide rule-based alerts. With this, the regulated entities must use systems that maintain audit trails for proper documentation of activities and customer records.
Further, regulated entities must design effective policies, procedures, and controls and define the adequate role of compliance teams to ensure accountability and transparency. Moreover, regulators expect entities to draw effective procedures for handling alerts and escalating suspicious activity for investigations.
Enhancing Anomaly Detection with Citadel365
Citadel365 provides integrated transaction monitoring and customer risk assessment software that checks customer behaviours to create a baseline and identifies unusual patterns or activities to detect anomalies. Further, it combines the customer profiles, screening results and transaction data to analyse customer behaviour and identify deviations such as a sudden rise in transfer volumes.
Citadel365 allows regulated entities to configure monitoring rules to define thresholds for detecting anomalies. With this, it also learns patterns from customers’ normal behaviours to identify deviations or unusual patterns for generating alerts.
Citadel365 case management software allows structured workflows for investigations, and its audit trail feature eases regulatory reviews, meeting AML compliance regulatory requirements.
Balancing Detection Accuracy and Operational Efficiency
Transaction anomaly detection systems must be fine-tuned by setting appropriate thresholds, risk scores, and alert frequencies to identify unusual patterns and reduce false positives. With this, the system must be capable of detecting unusual patterns from the customer’s normal business activity by learning from the customer’s past behaviour to improve future alerts.
Further, constantly optimising or refining anomaly detection helps identify new, evolving threat patterns that align with entities’ ML/TF risk exposure, thereby reducing operational burden and enhancing compliance.
Anomaly Detection FAQs for AML Professionals
Anomaly detection in AML compliance means using advanced tools to identify unusual transaction patterns, such as a sudden rise in transfer volumes and multiple small deposits.
Rule-based monitoring includes identifying deviations through predefined rules, while anomaly detection identifies unusual patterns that don’t match customers’ normal behaviour and appear suspicious.
Anomalies such as unusual, high-value transfers; cash deposits at unexpected intervals; deviation in customer activity from normal behaviour; and multiple small transfers to avoid thresholds often indicate potential financial crime.
Regulators find anomaly detection systems effective that flag deviations even without fixed thresholds, which helps minimise false positives, and identify trending or recent ML/TF typologies to prevent financial crime.
Yes, machine learning systems learn customers’ normal behaviour from historical patterns and detect unusual patterns that deviate from expected behaviour, which improves anomaly detection and supports AML compliance.