Dimitar Dimitrov is the founder and Managing Partner at Accedia, a leading European AI & Custom Software Development Company.
Scaling AI brings new opportunities, but it also brings decisions your institution must be prepared to defend. The growing use of large language models and AI agents raises questions about ethical AI use, privacy and customer protection. These concerns are already widespread: The Cambridge Centre for Alternative Finance’s 2026 report found that 74% of surveyed financial-services firms and 80% of regulators identified data privacy and protection among the leading AI risks.
As more models and information sources influence outcomes, accountability becomes harder to maintain. Gaps can remain hidden until a customer challenges a decision, or a regulator asks for evidence. I will examine what executives need in place to trace a decision, verify that the system operated as intended and intervene when it does not.
Why AI Accountability Gets More Complex Once The System Is Live
Once AI goes live, it works with changing data, interacts with other systems and affects real customers. A single decision may involve several tools and people, making it harder to establish what influenced the outcome and who was responsible.
Four factors add to this complexity:
• Multiple components influence the outcome. A decision may involve a model, retrieved information, business rules, external services and human judgment. Understanding the model alone may not explain the final result.
• Conditions change after testing. Data, models and connected services can change, while customers introduce situations that testing did not cover.
• Responsibility spans teams. Technology, risk, operations and external providers may each own part of the process. This is the factor that causes the most trouble. When a decision is questioned, each team can explain its own step, and no one can account for the outcome end to end.
• Reconstructing a decision requires evidence. To defend a challenged decision, you must be able to trace the inputs, system versions, checks and approvals involved. Gaps in that evidence can make the outcome difficult to explain.
Accountability has to cover the full decision process, including the systems, information and people that shape the outcome. Lloyds Banking Group is a good example of doing this: In April 2026, the bank reported recording every AI use case in a central inventory, with oversight extending from initial development through deployment and ongoing monitoring. The inventory is the part I would copy first. One list of every AI use case means a team can answer any of them the moment someone asks.
Which Boundaries To Set Before AI Goes Into Production
Before AI goes live, you need to define what it can do, who can intervene and how decisions will be traced. Regulatory expectations already point in this direction. Revised Federal Reserve, OCC and FDIC model-risk guidance excludes generative and agentic AI, but financial institutions are still expected to address these technologies through broader governance and risk-management practices. In the U.K., the FCA identifies existing senior management accountability rules as relevant to AI oversight.
In each AI implementation project I guide in financial services, I advise clients to answer four questions before deployment:
1. What is the system authorized to do? Set limits based on an action’s consequences and reversibility. Specify when AI can recommend or execute an action, when human approval is required and how its output can influence decisions in connected systems. A recommendation deserves closer scrutiny if downstream teams routinely accept it without challenge. Enforce these boundaries through system permissions and approval controls.
2. When must the process pause for review? Define triggers such as conflicting evidence, missing data or actions outside the approved scope. Give reviewers the context, time and authority to challenge an action, and establish what happens if no reviewer is available. Human approval provides little protection if the reviewer cannot independently assess the evidence or stop the process.
3. Who owns the process and can intervene? Assign an accountable owner for the complete process, including handoffs between teams and external providers. Define who monitors compliance with approved limits, can suspend operation and authorizes changes or recovery. That owner needs authority to resolve gaps between teams and require reassessment when a provider update changes system behavior.
4. What evidence must be retained? Start with what the institution would need to explain a challenged outcome. Link the original input to the final action through records of system versions, checks, approvals and human overrides. Before launching, ask a team uninvolved in development to reconstruct a decision from the information you provided. This tests whether the evidence is usable without relying on its creators’ memory.
Why Accountability Is Important For Reliable AI
Effective accountability helps financial firms gain speed from AI while maintaining confidence in the decisions it supports. It helps your team catch hallucinations before they affect customers and learn from the errors they uncover.
On a lending engagement at Accedia, I saw AI-generated summaries help staff review applications faster. Before launch, the client gave reviewers responsibility for verifying claims against source documents, and required discrepancies to be resolved before a summary could inform a decision. If the AI called income stable despite fluctuating figures, the reviewer had the evidence and the authority to correct it. That sequencing is the part I would insist on. Responsibility assigned up front made checking for hallucinations a standard part of the lending process from the first application.
Recording the correction and its supporting evidence also created a basis for improvement. Teams could investigate whether similar errors had affected other applications, add those cases to testing and verify that revised safeguards addressed the problem. This gave leaders a stronger basis for expanding AI use: evidence that both the system and its oversight were becoming more dependable.
Conclusion
Each time you give an AI system more room to act, you make a decision you will have to stand behind. Deloitte puts it plainly: “FS supervisors are looking to boards and senior managers to understand the risks” of AI adoption. I would expand AI’s authority only as far as I could still explain it. The firms that hold that line will keep moving while the others stop reconstructing what happened.
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