Regulatory & Policy Requirements Identification

Financial services companies have been among the earliest adopters of AI-enabled compliance systems. Amsterdam- based neobank bunq, which serves over 17 million users in the European Union, uses AI to boost fraud detection workflows and flag suspicious transactions that present risk of fraud or money laundering, according to AI chip maker Nvidia.

Healthcare and wealth-management firms have also embraced retrieval-augmented generation (RAG) for regulatory compliance. A global wealth-management firm partnered with Squirro to launch generative AI–based “employee agents” that assist 900 client advisors in interpreting regulations and making faster, data-driven decisions. These tools have proved especially useful where privacy and clinical regulations intersect, such as aligning healthcare data rules with GDPR obligations.

Industry research shows accelerating adoption of AI compliance technology. Among specialists in combating money laundering, 18% had already deployed AI tools in 2024 with another 43% either piloting them or planning to deploy them within 18 months, according to a survey of more than 850 compliance professionals, by software provider SAS, the Association of Certified Anti-Money Laundering Specialists and consulting firm KPMG. Asked why their organizations were primarily using AI, 36% said to improve the quality of investigations, 31% to reduce false positives, and 21% to detect complex risks that are currently undetected, while 13% cited other goals.

Consulting firm McKinsey estimates 60% of legal work can now be automated, and AI-driven tools can reduce review time by 70%. 253 3.2 Analyze