Comprehensive AML Detection Framework: Rules, Behavior, Patterns, and Risk Analysis
Rule based detection
Use predefined rules and thresholds to flag suspicious transactions. Examples include large cash deposits, frequent small transactions (structuring), or transactions in high-risk jurisdictions.
Pattern and Anomaly Detection
Identify recurring patterns indicative of money laundering, such as circular transactions or rapid fund turnover. Enhance detection accuracy by learning from historical data and investigator feedback.
Behavioral and Peer Analysis
Monitor customer transactions against their historical activity to detect unusual patterns like sudden increases in transaction volume or frequency.
Risk and Narrative-Based Analysis
Flag transactions linked to high-risk regions or sanctioned countries. Include cross-border transaction monitoring for jurisdictional risks. Verify customer identities, detect fraudulent activities, and flag inconsistencies during onboarding and account monitoring.
Custom Rule Creation
Allow investigators and analysts to design and implement custom detection rules tailored to emerging threats. Adapt to new money laundering typologies and regulatory requirements.
