The firms that lead in AI will not be those with the most pilots, but those who can connect every material AI use case to regulatory obligations, risks, controls, accountable owners and evidence, says Rohini Gupta, CEO of FinregE
The future of financial services AI will be won by governance, not experimentation.
HM Treasury’s Financial Services AI Adoption Plan marks a turning point in the UK’s approach to artificial intelligence (AI). The question is not whether banks, insurers, asset managers and other financial institutions will adopt AI, since so many already do. The more important question is how they can scale its use across core processes without undermining accountability, consumer protection or operational resilience.
The plan sets out scaling AI as a strategic imperative for UK growth and competitiveness. This requires a sector where businesses of all sizes can deploy AI responsibly and quickly, with more co-ordination between industry, government and regulators.
Regulatory clarity must become operational clarity
The Government paper says that the UK’s principles-based, outcomes-focused regulatory framework is an asset. Firms aren’t necessarily asking for a whole new AI rulebook. They want more explicit and practical guidance on how the existing requirements apply to real use cases.
This is significant because AI has the potential to affect numerous regulatory domains at the same time. A customer-facing tool may also raise concerns regarding Consumer Duty, data protection, operational resilience, model risk management, outsourcing and the Senior Managers and Certification Regime.
More centralised support and increased coordination between regulators would be beneficial. However, corporations will need to translate high-level expectations into day-to-day controls. For each material AI use case, they should be able to show:
- the relevant regulatory requirements;
- the risks, policies and controls that mitigate those obligations.
- The person who owns the use case and its results;
- how performance, exceptions and model changes are monitored; and,
- what evidence indicates ongoing compliance.
The industry is well advised to go from regulatory clarity to regulatory traceability. That means a visible link from the source requirement to the decision, control, owner and evidence.
AI governance should not become another silo
Many organisations are, quite rightly, creating AI policies, committees, inventories and risk assessments. They are needed but should not be isolated from the overall compliance and risk framework.
AI inventories should, in an ideal world. be connected to risk registers. Risk assessments need to be tied to controls and policies. Regulatory changes, meanwhile, should be assessed against impacted models and workflows. And material decisions should be kept with an auditable rationale.
Without those links, companies risk building a robust AI governance framework that is, in fact, disconnected from the systems through which they manage compliance. The result is duplicated work, inconsistent interpretation and limited visibility for boards and regulators.
Trust will become a competitive advantage
The plan also raises a significant question about the regulatory perimeter. Consumers are already utilising general-purpose AI solutions to budget, save, invest and make other financial decisions. Without the controls, accountability and redress that you would expect from regulated organisations, these tools can provide advice-like results.
The suggested assessment of general-purpose large language models, as well as the request for improved consumer disclosures, highlight a broader concern: customers may be unaware that they are engaging with a regulated service or not.
This is both a difficulty and an opportunity for regulated institutions. AIcan give more accessible and responsive financial assistance, but confidence must be established through controlled data, tested outputs, transparent accountability, human intervention and effective restitution. Responsible regulation can thus become a competitive advantage rather than a constraint.
Third-party AI risk is becoming systemic risk
Financial institutions are increasingly reliant on a small group of cloud, infrastructure, and foundation-model vendors. This concentration can result in operational, security and resilience risks that go far beyond a single organisation.
The proposal calls for a speedier implementation of the Critical Third Parties regulation, voluntary sharing of AI incidents and near misses, and an industry-led third-party assurance mechanism. These approaches have the potential to eliminate repeated evaluations while also improving collaborative learning.
However, common assurance can’t substitute for firm-specific accountability. Each organisation must identify where external AI is deployed, which critical business services rely on it, what happens if a supplier or model changes, and whether credible alternatives and exit strategies exist.
Agentic AI will test existing accountability models
The emphasis on agentic payments provides an early indication of the future governance dilemma. Autonomous systems can perceive goals, interact with other systems, and initiate actions with minimal direct human intervention.
The strategy establishes legal liability, “Know Your Agent” protocols, and secure machine-to-machine authentication as the cornerstones of trusted agentic payments. Similar questions will be raised as AI agents enter customer service, compliance, procurement, trading, and operational decision-making.
Firms will require clear, machine-readable boundaries for rights, restrictions, escalation triggers, forbidden actions, and data access. They will also require documents detailing what an agent performed, the information used, and why the action was approved.
What financial institutions should do now
Firms should not wait for all the plan’s recommendations to be implemented. They may reinforce their foundations immediately by following these five practical steps:
- Develop a comprehensive list of AI use cases, including vendor products and staff use of general-purpose AI.
- Align each material use case with appropriate regulatory duties and customer outcomes.
- Align obligations with policies, risks, controls, owners, and testing evidence.
- Consider both regulatory and technological changes when assessing compliance.
- Incorporate auditability and human oversight into workflows from the beginning.
The UK has an opportunity to define what trusted scale looks like
The UK has an opportunity to demonstrate responsible AI adoption on a large scale, rather than just quick adoption. Speed and governance should not be considered as opposing aims. Weak governance restricts innovation since every new use case must navigate fragmented data, duplicate assessments, and uncertain ownership. Strong governance offers established routes that enable innovation to grow more rapidly.
The next generation of financial-services AI will require coordination among technology, risk, compliance, legal, and business teams to view regulation and accountability comprehensively. The leading institutions will view regulatory intelligence and governance as infrastructure that enables AI to scale.
How FinregE helps firms move from AI ambition to governed adoption
FinregE assists financial institutions in developing the regulatory infrastructure required to use AI with greater confidence.
Its Regulatory Operating System (FinregE ROS) integrates regulatory intelligence, obligations, risks, controls, policies, assessments, and accountable owners into a traceable environment.
FinregE assists organisations by assisting them:
- Monitor regulatory developments across many countries.
- Utilise AI to assess and summarise complicated regulatory papers.
- Create machine-readable digital rulebooks from regulatory text.
- Link internal policies, risks and controls to regulatory obligations.
- Evaluate how regulatory changes impact corporate processes and technologies.
- Use regulatory workflows to assign actions and ownership.
- Maintain a visible audit trail from regulation to implementation.
- Ensure compliance, risk and business teams have a shared understanding of regulatory requirements.
FinregE’s AI RIG (Regulatory Insights Generator) is created for the environment in which regulated businesses operate. Rather than treating AI as a general-purpose answer engine, it enables users to collaborate with recognised regulatory sources and incorporate AI-supported analysis into controlled compliance processes.
This is an important distinction. The future of regulatory AI does not include autonomous systems that provide answers without context. AI functions in an environment where sources can be verified, outputs can be evaluated, responsibilities can be assigned and decisions can be documented.
FinregE integrates AI with horizon scanning, regulatory mapping, digital rulebooks and regulatory compliance workflows, enabling institutions to move away from fragmented interpretation and toward continuous regulatory traceability.
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