AI in Finance
AI Governance for Finance Functions: A Practical Framework
26 January 2026
Governance is the part of AI adoption nobody wants to discuss in the early enthusiasm phase. The conversation is about capability, efficiency, and ROI. Governance feels like the thing that slows decisions down and adds process to something that is supposed to be making the function leaner.
Then something goes wrong. An AI-assisted approval routes an invoice to the wrong cost centre at scale for three weeks before anyone notices. An AI matching tool posts a journal entry based on data it should not have had access to. An auditor asks for the documented logic behind an automated decision and the answer is a vendor’s black box. At that point, governance becomes all anyone wants to discuss.
Build it before you need it. The effort is modest. The alternative is managing a control failure while also trying to run a finance function.
What AI governance in finance actually means
AI governance is not a policy document. A policy document is a component of governance, but governance is broader and more operational than that.
AI governance in a finance context is a set of decisions, documented and maintained, covering: who can use what AI tools on what data; what level of human review is required before an AI recommendation becomes a decision; how errors in AI outputs are caught, corrected, and documented; who is accountable when an AI-assisted process produces a wrong outcome; and how you demonstrate to auditors that your AI-assisted processes are controlled.
The last point is increasingly practical rather than theoretical. External auditors are already asking about AI-assisted finance processes in their risk assessments. ISA 315, the international auditing standard on risk assessment, requires auditors to understand the entity’s use of technology in financial reporting. That includes AI tools that influence posting decisions, approval workflows, or reconciliation outputs. If your external auditor asks you to walk them through your AI governance framework next year and the honest answer is that you do not have one, that is an audit finding.
The governance framework does not need to be elaborate to be functional. It needs to answer specific questions clearly.
The four governance decisions every finance function needs to make
Before deploying any AI tool that touches financial data or processes, four governance decisions need to be made explicitly, documented, and communicated to the team.
Decision one: data access controls.
What financial data can the AI tool access? Who authorised that access and on what basis?
This matters because AI tools often request broader data access than their specific function requires. An invoice processing tool that requests read access to your entire ERP database rather than the AP module. A forecasting tool that wants access to payroll data to improve its model when payroll data was never part of the approved scope.
The principle is least-privilege access: the AI tool should have access to the minimum data required to perform its function. Define that scope in writing before you connect the tool to your systems. Review it at each renewal.
This is also where you document your data residency and processing decisions. Where is the vendor processing your financial data? What contractual protections govern that? What happens to your data if you terminate the contract? These are procurement and legal questions, but they are governance questions too. Finance data is sensitive. The governance around it should reflect that.
Decision two: human review thresholds.
At what value, exception type, or confidence score does human sign-off become mandatory, regardless of what the AI recommends?
Most AI tools have configurable confidence thresholds. You can set the system to auto-approve matching decisions above 90% confidence. You can set payment approvals to require human review above a certain value. You can set anomaly flags to escalate automatically rather than pass to automated resolution.
The specific thresholds matter less than the fact that they exist and are explicitly decided rather than defaulted. Vendor defaults are set for general applicability. Your thresholds should reflect your specific risk appetite, your transaction mix, and your regulatory environment.
Write down the thresholds. Review them quarterly in the first year as you understand the error distribution in your environment. A threshold that was appropriate at go-live may need adjustment once you have three months of production data on where the AI is making errors.
Decision three: error correction processes.
When the AI is wrong, what is the documented workflow for correction, and is it auditable?
This is the governance question that most implementations fail to answer adequately. The system goes live and the question of what happens when it makes a mistake is treated as something to handle at the time. The result is an ad hoc, inconsistent, undocumented correction process.
An error correction process needs four elements: a mechanism for identifying the error; a defined escalation path to the person with authority to correct it; a documented correction workflow with mandatory fields capturing what went wrong, why, and what action was taken; and a feedback loop to the AI tool or the team responsible for it so that recurring errors are addressed at source rather than corrected repeatedly.
The agentic AI governance post covers error correction in the higher-stakes context of AI that takes direct actions. The same principles apply to recommendation AI, with somewhat less urgency around the speed of the correction workflow.
Decision four: model change management.
When the vendor updates the model, what is your validation process?
This is a governance gap that is near-universal. Finance teams deploy an AI tool, validate it thoroughly at go-live, and then accept ongoing model updates from the vendor without any re-validation. A model update that changes how the tool handles a specific exception type, or that updates its training data, can materially change the tool’s behaviour in your environment without any signal that something has changed.
The minimum requirement is three things: a contractual notification obligation from the vendor when significant model changes are deployed; a defined validation run against a test dataset before the update goes to production; and a rollback procedure if the updated model produces worse outcomes in your environment.
For most AI tools in a finance context, a monthly validation run against 100 manually-reviewed transactions takes a few hours and catches most model drift issues. This is not onerous. It is the equivalent of monthly reconciliation discipline applied to your AI tools.
Agentic AI governance specifically
The four decisions above apply to all AI in finance. For agentic AI, where the tool takes direct actions rather than making recommendations, the governance requirements are more stringent.
The distinction matters. A recommendation AI looks at an invoice and says: this invoice should be coded to cost centre 1042 with high confidence. A human reviews that recommendation and approves or overrides it. An agentic AI looks at the same invoice and posts it to cost centre 1042. No human in the loop.
The performance upside of agentic AI is real: processing time falls, throughput increases, and the human workload concentrates on genuine exceptions rather than routine approvals. The governance stakes are also real: errors that a recommendation AI would have caught in the human review step are now live in your system before anyone sees them.
The governance principle for agentic AI in finance is this: recommendation AI needs oversight; action AI needs authorisation.
Oversight means a defined review cadence, human review thresholds, and audit trail requirements. Authorisation means a formal approval decision for every category of action the AI is permitted to take, with a named individual or role responsible for that authorisation decision, and a documented rationale for why autonomous action is appropriate at that value and that risk level.
For AP automation, this might mean: autonomous posting is authorised for three-way matched invoices from approved suppliers below 5,000 euro, with PO reference confirmed, from suppliers with a 24-month clean payment history. Everything outside those parameters requires human review. The authorisation for that autonomous action scope sits with the Finance Director and is reviewed quarterly.
That is a governance decision. It is not a vendor default. It is a deliberate choice with named accountability. The specific parameters will differ across organisations. The requirement for explicit, documented authorisation does not.
What you actually need to build
Governance does not need to be bureaucratic to be effective. The finance functions I have seen implement AI governance well do it with a small set of practical documents, consistently maintained.
A one-page AI use policy for the finance function. This covers: what AI tools the function uses; what those tools are authorised to do; what they are not authorised to do; and who to contact if there is a concern about an AI output. One page, readable in three minutes. Updated when tools are added or changed.
A data access register for AI tools. A simple register documenting every AI tool that has access to financial systems or data: what data it can access, when that access was authorised, who authorised it, and when the authorisation should be reviewed. This register is the answer to the auditor’s question about your AI data controls. It does not need to be more than a well-maintained spreadsheet.
A defined escalation path for AI errors. When someone on the finance team identifies a likely AI error, who do they tell? How is it logged? Who has the authority to make the correction? How is the correction documented? A simple escalation flowchart, circulated to the team and posted in your finance function’s shared workspace, serves this purpose.
These three documents constitute a functional AI governance framework. They can be produced in a day of focused work. They will satisfy the majority of external audit enquiries and provide a foundation for everything more sophisticated that follows.
Build from there as your AI footprint grows. The finance functions that struggle at scale are the ones where governance did not keep pace with capability. Keeping pace does not require a large investment of time or resource. It requires the same discipline that good finance practice has always required: document the control, operate the control, review the control.
The full AI governance context sits within the broader AI in Finance Strategy. Governance is not separate from strategy. It is what makes strategy sustainable.
The audit conversation
A note on how external auditors are approaching AI governance, because it is changing faster than most finance teams have noticed.
In 2024 and 2025, auditors began including AI-assisted finance processes in their IT general controls testing. The questions being asked: what AI tools does the finance function use? What financial processes do they influence? What human oversight exists? How are errors identified and corrected?
Finance teams with governance frameworks in place answer these questions and move on. Finance teams without them spend audit time rebuilding the logic behind AI decisions retrospectively, which is slow, expensive, and often incomplete.
The governance work described in this post is also preparation for that conversation. The data access register, the AI use policy, the human review thresholds, the error escalation paths: all of these are auditable records. They demonstrate that your AI-assisted processes are controlled, not just automated.
The managing audit effectively perspective applies here: the audit is easier to manage when the controls exist and are documented before the audit begins, not assembled during it.
Maebh Collins is a Fellow Chartered Accountant (FCA, ICAEW) with Big 4 training and twenty years of operational experience as a founder and senior finance leader. She writes about AI in finance transformation from the inside out.