FinregE warns UK AI plan needs stronger bank controls

FinregE has published an analysis of the UK's AI Adoption Plan 2026 for financial institutions, setting out five pillars for regulated AI adoption.

The report argues that firms face a gap between the regulator's broad aims for artificial intelligence and the practical demands of deploying AI within tightly controlled compliance structures. Many institutions, it says, are not yet structurally prepared to meet those expectations.

At the centre of the analysis is a warning that banks, insurers and other financial firms should not treat the UK plan as a simple compliance exercise. They instead need an operating model that links AI use, regulatory obligations, internal controls and accountability in a way that can be tracked and reviewed.

That approach is reflected in the report's five pillars: a comprehensive inventory of AI use cases; strategic alignment between each material use case and regulatory duties; operational mapping of those duties to internal controls and ownership; a holistic assessment of compliance as regulations and technologies change; and governance by design, with auditability and human oversight built into workflows.

The first pillar focuses on visibility. Firms are urged to create a full inventory of AI use across the organisation, covering not only formal systems bought from external vendors but also staff use of general-purpose AI tools.

The second and third pillars shift from identification to accountability. Institutions should map each significant use case to relevant regulatory duties and expected customer outcomes, then connect those obligations to internal policies, risks, controls, owners and testing evidence.

The fourth pillar addresses change management. Compliance assessments, the report says, should consider regulatory and technological change together rather than as separate workstreams, reflecting the speed at which both AI tools and rulebooks can shift.

The fifth pillar centres on governance. Audit trails and human supervision, according to the analysis, should be built into AI-related workflows from the start rather than added later.

FinregE presents the framework as an alternative to fragmented AI adoption within compliance teams. Its argument is that isolated tools may produce outputs, but do not by themselves provide the traceability regulators are likely to expect when institutions are asked to explain how decisions were made and who was responsible.

FinregE develops a regulatory operating system for highly regulated sectors, with a particular focus on financial services. The system brings together regulatory intelligence, obligations, risks, controls, policies, assessments and named owners in a single environment designed to show how rules are interpreted and implemented across an organisation.

According to the company, the platform monitors regulatory developments across multiple jurisdictions and uses AI to assess and summarise complex regulatory material. It also creates machine-readable digital rulebooks from regulatory text, allowing firms to link internal policies and controls directly to specific obligations and examine how legal and regulatory changes affect business processes and technologies.

That structure is intended to create an audit trail from the original regulation to implementation. FinregE argues that this model is better suited to regulated environments than general-purpose AI systems that produce answers without a documented compliance process around them.

Rohini Gupta, Chief Executive Officer of FinregE, said the issue for many firms is one of operating model design rather than simple AI enthusiasm. "The risk for many institutions is treating the regulator's plan as a checklist rather than a systemic shift in their operating models," Gupta said.

She added that compliance foundations must keep pace with the technology being introduced. "For AI to meet regulatory standards, the underlying foundation must be as dynamic as the technology it governs," Gupta said.

The report also addresses the use of AI tools in regulatory interpretation. Firms, it says, should rely on systems designed for controlled compliance workflows, where source material is known, outputs can be evaluated, responsibility assigned and decision-making recorded.

Gupta expanded on that view.

"The future of regulatory AI does not lie in autonomous systems that provide answers without context," she said. "It requires environments where sources are verified and outputs are evaluated, responsibilities are assigned and decisions are documented. By integrating AI with horizon scanning and regulatory mapping, we enable institutions to replace fragmented interpretation with continuous regulatory traceability." 

FinregE was founded in 2018 and says its systems analyse more than three million regulatory data points from more than 2,000 sources across more than 160 jurisdictions.