Financial services is moving from AI that answers questions to AI that takes action – and that shift changes what accountability means. The Government’s Financial Services Adoption Plan calls on the sector to move beyond isolated pilots and scale AI across core processes.
AI agents could reshape how financial services operate, from investigating fraud alerts to processing claims and supporting compliance teams. But as AI moves from generating answers to executing actions, firms need to know not just how a decision was reached, but what happens next.
Head of Financial Services for UKI at UiPath.
Explainability remains a central question for AI in Financial Services and that becomes more complex as AI begins, in the words of Financial Conduct Authority (FCA) Chief Executive Nikhil Rathi, to “coordinate and transact”.
Model explainability is therefore only half the picture. Firms also need operational accountability across the workflow, with agentic business orchestration helping provide visibility into the systems an agent accessed, the actions it took, where human judgement intervened. That visibility is what allows agents, people and existing systems to work together under defined rules.
AI’s black box is moving into the workflow
This is a live concern. 48% of UK financial services executives say their firms are using agentic AI, yet, more than a quarter report having no or limited controls to ensure compliance with laws and regulations.
That gap matters because agents create a more complex governance challenge. They can move between systems, interpret information, select tools and determine what happens next.
The result can be a black-box journey flow where decision-making is distributed across multiple tools and systems rather than traceable to clear owners.
Take an insurance claim. One agent might collect evidence, compare it against a policy and route the claim for enhanced scrutiny. Another could contact the customer or recommend withholding payment.
But where was the consequential decision made? If challenged, the organization needs the visibility to reconstruct the journey.
A small error by an AI agent can be repeated and amplified. The real risk is not the hallucination itself, but what it sets in motion. A single incorrect detail could trigger an account restriction, customer communication or regulatory report before anyone spots the mistake.
That logic reflects the FCA’s operational resilience rules, which expect firms to map important business services, identify vulnerabilities and understand how disruption could travel through them. Agentic AI requires the same visibility into how failures move through an automated journey.
Accountability cannot stop at the AI model
As AI operates across more systems, accountability needs to follow the work rather than stop at the model.
In January, The Treasury Committee warned that the UK’s ‘wait-and-see’ approach to regulating AI in financial services could expose consumers and the wider system to serious harm. It also called for clearer guidance on consumer protection and senior managers’ responsibilities.
Firms need controls that work at the level and speed of the process, with clear ownership and an auditable record of the decisions and actions taken.
The challenge is that no single team may have a complete view of an agentic process. Compliance may understand the policy, technology teams the model, operations the customer journey, and third-party provider part of the infrastructure. An agent can move across all four in seconds. This is the organizational face of what's increasingly called agent sprawl – agents multiplying across systems faster than any single team can track them.
A senior manager can therefore remain responsible for an outcome while lacking the evidence needed to understand how it happened.
A human-in-the-loop isn’t the same as meaningful oversight
‘Human-in-the-loop’ is often used as shorthand for responsible AI – placing a person at a point in the process to review or approve an AI-generated decision.
But meaningful oversight depends on what that reviewer can see and whether they can intervene. An employee receiving an agent’s recommendation may not know which systems it accessed, whether the information was complete or what actions have already been taken. A credible-looking recommendation can quickly become a rubber stamp.
Controls should reflect the consequence of the action. Low-risk administrative work may only require monitoring. Restricting a bank account, rejecting an insurance claim or escalating a fraud allegation should demand a much higher threshold for human oversight.
The reviewer needs evidence behind the recommendation, a record of earlier steps and the ability to challenge or reverse the action. The goal is not to put a human click in front of every task, but to make human judgement count where the consequences are the greatest. Agents can reason and robots can act, but only people can lead – and leadership requires visibility, not just a veto.
Audit the journey, not just the outcome
Traditional audit records often capture what went into a process and the final decision that came out. Agentic workflows require a record of everything in between.
Firms need to know which agent acted, what information it retrieved, which systems and tools it used, what rules applied and where a person reviewed or overrode an action. Orchestration can bring those interactions into a consistent record, so actions across the workflow can be monitored, constrained and, where necessary, stopped.
That visibility must extend across third parties. Where an external service supports the workflow, the regulated firm still needs enough information to investigate an outcome and show its controls operated as intended.
Control before autonomy
Agentic AI can remove repetitive work and help employees focus on cases that need human judgement. Scaling that value depends on firms being able to see and govern what their agents are doing. That balance is sometimes described as controlled agency – autonomy that is real, but bounded by visibility and clear limits.
Every consequential workflow needs clear ownership, defined permissions and an audit trail. Firms also need the ability to intervene when behavior moves outside approved limits.
As financial services moves from AI that advises to AI that acts, the strongest systems will be those whose actions can be traced, challenged and stopped. That is what allows firms to increase autonomy without losing accountability – and scale agentic AI with confidence.
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