FCA turns to AI as anti-money laundering remit expands

The U.K. Financial Conduct Authority is expanding its use of artificial intelligence in supervision and enforcement as it prepares to take on anti-money laundering oversight of about 60,000 additional businesses, according to the Institute of Chartered Accountants in England and Wales. The increase would sit alongside the roughly 35,500 firms the FCA already regulates, materially enlarging the agency's monitoring burden across financial and professional services.

HM Treasury has decided that the FCA will become the single anti-money laundering supervisor for lawyers, accountants and trust and company service providers, according to the Law Society. ICAEW said that responsibility is currently divided among about two dozen professional bodies, meaning consolidation would shift a broad professional-services compliance regime into one regulator.

The operational pressure is clear. The FCA would need to assess a much larger volume of firm data, filings and risk signals. AI tools can help by reading incoming material, ranking risk and directing supervisory staff toward cases that appear to warrant earlier review. The mechanism is triage: software narrows a larger pool of information into a smaller set of priorities for human examiners and investigators.

FCA chief executive Nikhil Rathi said in a June 24 speech that the regulator is examining agentic AI as a “first responder” for wholesale-market monitoring. He said the FCA handles a billion rows of data a day and wants to combine technology with supervisory judgment to detect market abuse more quickly. “Technology is moving much faster than many regulatory paradigms,” Rathi said.

The FCA also said in its 2026/27 annual work programme that it plans to use generative AI to streamline supervision, accelerate authorisations and improve how it triages information submitted by firms.

Compliance gaps increase the supervisory challenge

The transfer comes as many professional-services firms show weaknesses in anti-money laundering controls. FTI Consulting found that, among accountancy and legal firms assessed in 2024-25, 24% of accountancy firms and 29% of legal firms were fully compliant with money laundering rules.

Those figures help explain why regulators are looking beyond staff increases. A larger supervised population, combined with uneven compliance, requires agencies to decide which firms deserve attention first. AI does not determine legal liability, but it can help regulators sort large data sets and allocate examination resources where risk indicators appear stronger.

U.S. supervisors face similar capacity pressures

The same capacity problem is visible in the United States. The Federal Deposit Insurance Corp. reported in March that it supervises about 2,755 state-chartered banks and conducted about 825 consumer compliance examinations in 2025. That leaves many institutions outside a consumer compliance examination in any single year.

The FDIC also said its consumer response unit closed 32,128 written complaints and call records, up 21% from 26,451 in 2024. Rising complaint volumes add to the information regulators must review between scheduled examinations.

The Federal Reserve faces a related issue. At the end of 2024, it supervised 651 state member banks within community banking organizations and 43 within regional organizations, according to the Fed. Because many banks are examined on cycles, filings, complaints and transaction information continue to arrive between on-site or formal reviews. Continuous monitoring systems could help supervisors assess new information as it is received and identify institutions that may need earlier attention.

Corporate finance teams are adopting similar tools

Adoption of agentic AI is also rising in the private sector. PYMNTS Intelligence reported that use of agentic AI in the services sector increased from 4.3% in August to 25% in November, while adoption among technology firms rose to 30.8%.

Finance departments are applying similar systems to internal controls and liquidity oversight. PYMNTS Intelligence found that nearly half of chief financial officers use AI to monitor working capital and cash flows continuously. Regulators are now trying to apply comparable tools to their own core task: identifying risk across a growing body of supervised firms before problems become harder to contain.

This story draws on original reporting from PYMNTS.