When AI Starts Transacting, Who Is Accountable?

The world of finance is moving toward agentic AI, where AI doesn’t just answer questions but actually makes purchases and negotiates on your behalf. Combine this with invisible finance, and banking disappears into the background of daily life. It’s a significant leap forward from opening an app or filling out forms to having your car, work software, or a secure digital identity wallet handling payments and loans instantly and automatically.

That’s where we’re headed to – with the global market for gigantic AI in financial services expected to grow at an average annual rate exceeding 40%, surpassing $80 billion by 2034. In a few years, we’ll stop doing banking and start overseeing systems that manage our financial lives for us. As AI systems move from advising users to executing transactions on their behalf, fintechs must confront a fundamental question: when a machine makes a financial decision, who carries the legal and regulatory liability?

The shift from assistance to agency

For finance, which has traditionally required humans to be present at the moment of transaction, it would have been once unthinkable to entrust machines with the agency to determine if, when, and how to transact – without the moment of decision requiring human discretion.

Invisible finance has already evolved through embedded payments, automatic subscriptions, one-click checkout, and real-time rails. Banking increasingly moved into products from inside banking apps. Combine this with agentic systems, and you get goal-driven financial capabilities that understand context, gather relevant information across platforms, and initiate workflows autonomously. In short, agentic finance transforms human intent into dynamic, continuous decision-making without requiring real-time human input.

Transactions, as we know them, are becoming more background infrastructure and less conscious interaction.

What are the implications?

The rise of agentic finance can be viewed through the lenses of control, behaviour, and trust.

Control isn’t about opening apps or clicking buttons anymore, but it’s absorbed into the invisible layers of identity, payment, and automation systems, guiding how money moves. It’s no longer exercised at the point of transaction but much earlier, when people define their preferences, limits, goals, and permissions. Instead of deciding each time money should move, they decide the rules under which it can. The system then carries that control forward, interpreting those rules in real time and acting accordingly.

This fundamentally changes and even challenges the way users exercise control. While control once lay in action, it now gravitates toward configuration. You are not managing transactions, but instead, you are setting the conditions under which transactions are allowed to happen. Oversight becomes reviewing and adjusting these conditions rather than approving payments one by one.

For fintechs, this changes where responsibility lies. Control is no longer housed in the interface but within the infrastructure itself. It lies in how identity is verified, how permissions are designed, how decisions are logged, and how actions can be audited or reversed. These layers shape how financial control is actually exercised, even if users never directly see it. Consequently, control is redirected into the pre-emptive auditability of the agent’s logic. This moves oversight from real-time transaction approval to the governance of ‘objective functions’, the core goals programmed into the AI, ensuring that the machine’s fundamental intent remains aligned with the user’s long-term interests before a single cent moves.

When financial actions move into the background, the way people interact with their money changes, too. Fewer things to manage, fewer prompts to approve, and fewer reasons to check in. Over time, the habit of actively managing transactions gives way to periodically reviewing how the system is operating. If cashless payments made transactions effortless and auto-renewals made them continuous, then agentic systems make them autonomous.

What then becomes of trust? As the user evolves from earlier routines of oversight, the reliability of the underlying system becomes the linchpin of trust. People are no longer judging a service by how reliably it processes a payment, but by how confidently it can be allowed to decide on their behalf. Users will want to know how decisions are made, what data is being considered, what boundaries exist, and what happens when something goes wrong.

What happens when something goes wrong?

Most financial law is built around the idea that humans intentionally initiate transactions. But when the moment of intent and the moment of execution are separated, this assumption weakens. With autonomous systems, the initiating act becomes indirect. The user may have authorized a broad set of rules, but not a specific transaction. So when something goes wrong, the exact decision that led to it becomes difficult to pinpoint. The idea of the single, clear decision-maker no longer holds, and the clear chain of intent, execution, and causation that legal frameworks have always relied on is disrupted.

Agentic systems introduce algorithmic interpretations of user intent and outcomes that emerge from real-time data rather than explicit instructions. What looks like a single transaction may in fact be the result of multiple automated judgments layered over time.

This creates practical challenges. For one, disputes become harder to untangle because it is unclear whether the issue lies in the user’s original configuration, the system’s interpretation of that intent, the data it relied on, or the action it ultimately took. Regulatory enforcement also becomes more complex, as traditional frameworks of authorization and accountability do not transfer neatly onto agentic decision-making.

Yet in the eyes of the regulator, the financial institution remains accountable for failures, breaches, or harm caused through these systems. The law treats AI’s actions as if they were carried out by a human employee. If the AI makes a mistake, the company bears responsibility, especially if the error arises from poor setup, misconfiguration, or insufficient oversight. Quality assurance and human supervision can thus never be downplayed in the face of autonomous decision-making. If anything, they become even more critical to ensure that systems act as intended.

It means being held answerable for decisions made by software that is designed to act independently, often in situations no human explicitly foresaw. The questions of liability, auditability, and explainability will move from the legal fringe to the very centre of design. Financial institutions will need clearer models to trace decisions, attribute responsibility, and demonstrate that even autonomous actions can be understood, reviewed, and governed. To bridge this accountability gap, the industry should adopt a ‘Rebuttable Presumption of Algorithmic Malfunction.’ This framework legally assumes a system error has occurred in any disputed transaction unless the financial institution can provide an immutable audit trail proving the agent strictly adhered to its encoded guardrails.

Having a senior person oversee every ‘agent’ helps manage the risk of unintended actions and prevents errors from escalating into real problems. This ensures the firm stays on the right side of the law while maintaining accountability.

What’s the ideal way forward?

As agentic AI makes its way into finance, governance must become equally explicit. Legal and compliance teams will need to play a proactive role in designing authorization frameworks for AI agents, defining liability across partners, setting contractual boundaries for machine actions, and establishing documentation standards that clearly outline who is responsible for what. Consent, too, needs to evolve – users must have a transparent understanding of what they are signing up for and the limits of agentic authority.

Ideally, it’s a world where the customers remain fully in control and always in the know of what exactly their AI agent is doing. Instead of relying on long, confusing contracts signed once, consent becomes dynamic and granular, granted through “micro-permissions” for specific tasks. However, to avoid the risk of ‘notification fatigue’, where users reflexively approve prompts without reading them, consent must be bolstered by hard-coded risk thresholds. These act as automated ‘circuit breakers,’ halting any non-deterministic or high-variance action that falls outside of a user’s historical behavioral profile.

For example, a user might allow their AI agent a digital “hall pass” to spend only up to EUR 50 on their behalf for one day. Every action is logged, creating a clear trail that proves the AI stayed within authorized limits. If the AI attempts anything unusual or risky, the system automatically pauses and requests a quick confirmation through a thumbprint or face scan, for instance. Micro-permissions turn what could have been a legal headache into a real-time safety measure – a win-win for users and institutions alike. Users retain visibility and control, while AI autonomy operates within clear, accountable boundaries. This visibility is best maintained through ‘Continuous Verification,’ where a rule-based ‘Guardian’ layer operates in parallel to the AI agent. This secondary layer does not initiate transactions but possesses the absolute authority to veto any action that breaches predefined safety boundaries, ensuring human-centric safety remains proactive rather than merely logged.

Ultimately, the success of agentic finance will depend on its ability to operate safely, reliably, and in a human-centered way. The challenge lies in turning a complex, invisible system into something people can trust, understand, and feel in command of.