The Consumer Does Not See the Perimeter
The UK’s obvious AI-finance risk is easy to picture: a bank releases a chatbot, calls it an adviser, and the Financial Conduct Authority decides whether the product is inside the regulated advice perimeter.
The harder problem is the model that is not a bank product at all. A consumer opens a general-purpose LLM, pastes in a pension statement, asks whether to consolidate accounts, then asks which fund looks cheaper. The tool may not be marketed as financial advice. It may not be deployed by an authorised firm. It may have a disclaimer. The user still experiences a guided financial decision.
That gap is now visible in the FCA’s own perimeter work. In its 2026/27 perimeter report, the regulator says general-purpose large language models can help consumers make financial decisions and flags this as a cross-sector perimeter issue. The important phrase is not “LLM.” It is “general-purpose.”
The UK already has rules for authorised firms using models in customer journeys. It has rules for regulated advice. But the next problem is not neatly housed inside a bank, broker, pension provider, or insurer.
The Distinction Matters
The FCA draws a useful line in the perimeter report. It distinguishes consumers using general-purpose LLMs to help with financial decisions from LLMs that are specifically deployed to provide regulated financial advice. That distinction looks technical. It is the whole story.
If a regulated firm deploys an LLM to provide financial advice, the perimeter question is familiar. The firm, the activity, and the customer relationship are legible. The regulator can ask whether the system is giving a personal recommendation, whether the firm has the right permission, and whether suitability rules apply.
General-purpose LLMs scramble that setup.
They can produce financial guidance without being finance products. They can sound confident without having a duty to assess suitability. They can translate dense disclosures into plain English, then move from explanation into recommendation with one follow-up prompt.
The regulatory perimeter depends on categories. Is this advice or information? Is it personal or generic? Is it given by an authorised person? Those questions still matter. They become harder to apply when the interface is a general model trained to be helpful across every domain at once.
This is why the perimeter problem is not solved by telling consumers to look for FCA authorisation. That works when the consumer is choosing a broker. It is weaker when the consumer is using a general assistant to interpret their own documents.
The protection mismatch is psychological as much as legal. A regulated adviser is constrained by rules the consumer may never read. A general LLM is constrained by product policy, training, retrieval quality, and whatever guardrails the provider has chosen. Both can appear in the same chat-like format. Only one comes with financial-advice accountability.
Open Finance Makes the Gap More Useful, and Riskier
The FCA’s open-finance work makes the perimeter issue more urgent. In its open finance feedback statement, the regulator links high-quality financial data to future consumer and business use cases, including agentic AI. That is the positive version of the story.
Open finance could let consumers port verified financial data into tools that budget better, find cheaper products, and reduce friction in lending. The point is not just more data. It is cleaner, permissioned, machine-readable data. Agents become much more useful when they are not guessing from a PDF screenshot.
But the same quality that makes open finance productive also makes general-purpose AI more consequential. A model with accurate balances, product terms, income, debt costs, and investment holdings can produce sharper guidance. It can also produce more persuasive bad guidance.
This is the perimeter gap in its practical form. The more complete the data, the easier it is for an assistant to behave like an adviser. The more agentic the tool, the easier it is for guidance to become action. A user asking “what should I do next?” may get a ranked set of steps that looks operationally close to advice, even if the provider insists it is only educational support.
The FCA is not anti-data here. It is trying to create the conditions for safer data sharing. The tension is that open finance and general AI are moving toward the same user experience: a consumer delegates more of the analysis to software.
Disclaimers Will Not Carry the Load
The obvious industry response is to rely on warnings. Do not provide financial advice. Consult a professional. Check regulated sources. This may reduce legal risk. It does not resolve user reliance.
LLMs are not static content pages. They ask follow-up questions. They adapt the answer to the user’s circumstances. They can compare options, summarise risk, and produce a plan. A disclaimer does not change the interaction pattern.
The FCA’s consumer-facing material already shows the regulator understands that people are using AI for investment research. Its InvestSmart guidance on using AI for investment research warns that AI tools can be inaccurate and that consumers should check information before acting. That is sensible retail guidance. It is not a perimeter solution.
The deeper question is what happens when a general assistant repeatedly gives finance-specific outputs that consumers predictably treat as decision support. Regulators do not have to decide that every such output is regulated advice to care about the pattern.
That is a difficult posture for the UK. Move too aggressively and the regulator risks pulling general software into finance regulation by accident. Move too slowly and consumers may assume protections that do not exist.
The better answer is likely not one giant redefinition of advice. It is a stack of narrower expectations: clearer product labelling, sharper restrictions on regulated firms embedding general assistants, audit trails for finance-related model outputs, stronger handoffs to authorised advice where personal recommendations are likely, and data-access controls for open-finance inputs.
Good perimeter work is mostly deciding where the boring tripwires belong.
The Bank Chatbot Is the Easy Case
Regulators like supervised entities. Banks, insurers, brokers, and pension firms can be examined, fined, and forced to remediate.
The general-purpose assistant has fewer handles. It may sit outside the UK. It may serve millions of non-finance prompts for every finance prompt. It may be integrated into search, productivity software, messaging, or a phone operating system rather than sold as a financial product.
That is why the FCA’s perimeter report matters. It suggests the regulator sees the next boundary problem before it becomes a tidy enforcement case. The perimeter issue is not just whether an LLM can give bad advice. Humans can do that too, with worse formatting.
The issue is whether AI systems will make advice-like guidance cheap, personalised, and socially normal while the legal protection model remains attached to authorised firms and defined regulated activities.
Open finance will sharpen that problem. Agentic AI will sharpen it again. The UK can get real consumer value from both. But it cannot pretend the perimeter is still obvious just because the chatbot is not wearing a bank logo.
The next advice problem will arrive as ordinary software. That is exactly why consumers may not know when they have stepped outside the fence.
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