McKinsey reports that agentic AI could cut banks' transaction monitoring workload by 30-50% while improving compliance report quality.

Banks have seen more than 40% reductions in human processing time for transaction monitoring investigations, with AI-generated reports showing higher completeness against predefined checklists.

Adoption should start with a focused use case, demonstrate value, then scale to broader compliance tasks, while ensuring data access, staff collaboration with data scientists, governance, and ongoing monitoring for reliability and evolving requirements.

Human oversight remains essential; humans must assess findings, address gaps, and retain decision responsibility, even as AI analyzes evidence and makes recommendations.

AI agents would handle data collection, research, analysis, and report preparation, enabling human experts to focus on complex cases, risk assessment, and final decisions.

The same approach could extend to fraud detection, know-your-customer checks, and regulatory compliance, with examples such as a large bank halving time spent on corporate credit risk assessments.

Agentic AI coordinates entire investigations across systems, from information gathering to preparing documented recommendations, and can aid in responding to sophisticated financial crime threats.

Banks should design systems with quality checks, risk controls, and traceable decisions, noting that 30-50% of an agent network may be dedicated to QA and traceability.

AI-generated reports offer greater consistency by using uniform analysis periods and report structures, with sources linked for review by quality teams, auditors, and regulators.