A significant divide is opening up between the two leading AI providers in UK banking. OpenAI is granting several additional UK banks access to its GPT-5.5-Cyber model, while rival Anthropic continues to limit access to its more powerful Claude Mythos Preview.
The contrast reflects two competing theories about how AI providers earn trust in regulated markets: broad deployment with responsible use guidelines versus tight controls with limited early access. But it also points to something larger. Agentic AI tools are actively competing for position in one of the world’s most regulated industries, each promising to change how banks operate, serve customers, and manage risk. Here is what each tool brings to banking & fintech, and what the opportunities and risks look like in practice.
What Separates the Leading Agentic AI Tools
Not all agentic AI tools work the same way, and in the banking field, those differences translate directly into what gets automated, how safely, and with what level of oversight.
- Claude (Anthropic) is built for precision in high-stakes, document-heavy work. Banking generates vast amounts of data, and for AI to be useful in this context, that data needs to be clean, structured, and backed by financial data governance practices. That foundation is what makes Claude effective for contract review, regulatory compliance checks, and decisions that require a clear audit trail. In an industry where regulators can ask a bank to explain exactly how a decision was reached, that auditability matters.
- ChatGPT (OpenAI) is the broadest operator of the group. It can reason across multiple steps, pull from different data sources, and complete complex workflows autonomously, covering everything from assessing whether someone qualifies for a loan to flagging suspicious transactions. Its wide range of integrations makes it the default starting point for banks testing agentic AI at scale.
- Gemini (Google) can process the equivalent of thousands of pages of documents in a single session, thanks to its 1 million token context window. For banks dealing with large volumes of financial records or research, that means faster answers from material that would previously have taken a team days to work through.
What Would Agentic AI Tools Bring to Banking & Fintech
The recent moves of major UK banks & fintechs reflect how seriously embedded AI has become across the sector. HSBC appointed its first Chief AI Officer, Starling launched the UK’s first agentic AI financial assistant, and Revolut rolled out AIR to 13 million UK clients, all within weeks of each other. The technology is being prioritised on two fronts simultaneously: improving what customers experience and reshaping how financial teams work.
However, while impressive the current pilots are narrow by design. What they are testing, at а small scale and in controlled conditions, points toward changes that go well beyond the current scale.
Personal finance runs itself. Imagine an AI agent that monitors spending, flags unusual transactions, moves surplus cash into savings, and renegotiates subscriptions autonomously. That is a fundamental shift in the relationship between a bank and its customers. The bank stops being a place people go to manage money and starts being a system that manages it for them.
Financial crime gets harder to commit. FIS announced in May 2026 that it is working with Anthropic on an agent that compresses anti-money laundering investigations from hours to minutes, automatically assembling evidence and surfacing the highest-risk cases for review. Scaled across the industry, that changes the speed of fraud detection, as well as the economics of running a compliance function entirely.
Credit can be approved in minutes. Loan approvals that once took days are moving toward same-day decisions, as agents pull together the relevant data and return an answer without a human manually working through each step. For individuals, that means less waiting and fewer document requests.
What are the Potential Risks of Agentic AI Tools in the Financial Industry
Those use cases may be appealing to financial users and banking decision-makers, but the same qualities that make them valuable introduce new risks. Without a clear agentic AI strategy and defined boundaries for what an agent can and cannot do, speed and autonomy become liabilities as much as advantages.
An agent that can move money or make financial decisions autonomously is only as trustworthy as the rules and data it operates within. If those rules are poorly defined, the agent can easily misread a situation. AI hallucinations add another layer of risk, causing systems to generate inaccurate conclusions with a high degree of confidence. In financial environments, those mistakes can have immediate consequences for customers before anyone has the chance to intervene.
Data privacy is another consideration. Agentic AI systems need access to detailed financial information to function. Clients should understand what data the agent can see, how it is used, and whether it stays within the bank’s own infrastructure.
The speed at which agentic AI operates magnifies the consequences of any error. Customers should be cautious about what data, permissions, and account access they grant to any AI-powered service, and read the terms before opting in.
Where All of This Leaves Modern Finance?
Agentic AI is already running in production across the UK finance industry, and the pace is not slowing down. The competition between OpenAI, Anthropic, Google, and others is pushing capability faster than regulation and trust are keeping up. The technology will deliver on its promise, but how safely and for whose benefit will depend on the transparency built around it, and that conversation is still very much open.
