Using AI to Solve Complex AML Challenges: False Positives, OSINT, Sanctions, and UBO Analysis

Anti-money laundering compliance teams are under increasing pressure from high alert volumes, complex ownership structures, evolving sanctions regimes, and growing expectations around investigative depth. Artificial intelligence is now emerging as a practical tool to improve efficiency and decision-making across the AML lifecycle — but its use must be carefully governed to manage regulatory and operational risk.

This advanced session explores how AI can be applied to core AML challenges, including false positive reduction, OSINT and adverse media analysis, sanctions screening, beneficial ownership identification, and enhanced due diligence. It also addresses the practical limitations, governance requirements, and risk controls necessary to deploy AI effectively within regulated financial environments.

Key Topics Discussed

• AI-driven false positive reduction and alert triage
• OSINT and adverse media analysis using AI tools
• Source of wealth and source of funds analysis
• Sanctions screening and evasion detection techniques
• UBO identification and ownership structure analysis
• Network and relationship mapping in AML investigations
• AI-assisted enhanced due diligence workflows
• Practical limitations of AI in compliance environments
• Governance, oversight and model risk considerations
• Regulatory expectations for AI in AML programs