How do we regulate payments when it's AI agents spending the money?

A small business owner asks an AI assistant to manage a simple task: review outstanding invoices, check which suppliers need to be paid, and prepare the next set of payments. The assistant scans emails, compares due dates, and prepares recommendations for approval.

For the business owner, this could remove hours of repetitive work.

But for the financial institution processing those payments, it changes the nature of trust. A payment may still come from a legitimate account, but the decision behind it may have been shaped, prepared or initiated by software acting on someone’s behalf.

This creates a significant challenge for financial services. It’s no longer enough to simply understand who or what is behind a transaction; institutions now need to be able to discern whether the action reflects genuine human or business intent.

This challenge becomes particularly important in payments.

The International Monetary Fund has described agentic AI as a development that could move payments from human-initiated instructions towards agent-mediated decisions. Payments are not just another automated workflow. When money moves, the consequences are immediate and can be difficult to reverse. Financial systems have long been built around the assumption that a payment instruction can be linked to a person, business or institution with clear authority to act. Agentic AI complicates that assumption by adding another layer between the human and the transaction.

This does not mean AI agents should be kept away from financial activity. Used carefully, they could help people and businesses manage routine financial tasks with less friction. But the more useful these systems become, the more important it will be to define the conditions under which they can act. The question is not whether AI agents should be used in finance, but how institutions can support their use without weakening accountability.

In 2026, Santander and Mastercard demonstrated Europe's first live payment executed by an AI agent within a regulated banking environment. The transaction was completed using pre-authorised customer permissions, tokenized credentials and existing banking controls, illustrating that autonomous agents can operate within established regulatory and security frameworks rather than outside them. Similar initiatives announced by BBVA with Visa and Nordea with Mastercard suggest that financial institutions are increasingly exploring how AI agents can act on behalf of customers while maintaining the governance, authentication and auditability expected of regulated financial services.

An agent that can act quickly across several systems can also make mistakes quickly. It may be able to be manipulated through false information, compromised instructions or fraudulent requests that appear legitimate. In a payments environment, the difference between helpful automation and harmful activity may come down to whether institutions can understand intent, authority and context before money moves.

Today, many financial controls focus on identity. Who is the customer? Is the account legitimate? Does the transaction match expected behaviour? These questions will remain essential, but they may no longer be enough. If an AI agent is acting on behalf of a person or business, institutions will also need to understand whether the action reflects a genuine instruction, whether it sits within the agent’s permitted role and whether there is enough evidence to explain why the transaction happened.

This is where trust in agentic finance will need to be designed carefully. Stronger authentication will matter, but so will clearer audit trails. Financial institutions will need to know when an AI agent was involved, what it was authorized to do and whether its action can be traced back to a legitimate human or business decision.

Explainability will also become more important. If a transaction is blocked, delayed or flagged for review, customers and compliance teams need to understand why. If a suspicious transaction is approved, institutions need to understand what signals were missed. Black-box decision-making is uncomfortable in any regulated environment, but in financial services, where decisions can affect people’s access to money, markets and essential services, it becomes a direct trust issue.

There is also a human dimension inside financial institutions. Compliance and fraud teams are already working under pressure from faster payments, rising alert volumes, and more sophisticated criminal behaviour. Agentic AI could help them identify patterns, summarise cases and prioritize risk. But it should support human judgement, rather than replace it.

Regulation is evolving alongside these technological advances. In the UK, the Competition and Markets Authority has published guidance making clear that organizations remain accountable for the actions of AI agents acting on their behalf. Across Europe, the EU AI Act reinforces requirements around transparency, human oversight, governance and record-keeping for higher-risk AI systems. Together, these developments point towards an emerging model of autonomous compliance, where AI agents are expected to operate within robust governance and audit frameworks.

The World Economic Forum’s AI Playbook for Financial Services argues that trust, governance and human oversight are becoming critical tests as financial institutions move from experimentation to scaled AI adoption. Agentic AI will make those tests more demanding. It will require institutions to define where autonomy is acceptable, where human approval is still needed and how responsibility is recorded when software acts on someone’s behalf.

The future of trusted transactions will not depend only on knowing who someone is. It will also depend on understanding what they intended, what their AI agent was allowed to do and whether the action can be explained after the event.

As AI agents become more involved in economic activity, financial trust will need to evolve with them. The task ahead is not to slow down progress, but to make sure that as AI begins to act, the financial system can still answer one of its most important questions: should this transaction be trusted?