In a recent interview, Mark Babington, Executive Director of Regulatory Standards at the UK’s Financial Reporting Council (FRC), discussed what AI means for auditors, finance teams, and regulators.
Artificial intelligence is rapidly finding its way into financial reporting, consolidation, audit, and close processes. But as adoption accelerates, regulators are starting to ask tougher questions.
While the interview covered a wide range of topics, this blog covers the three key takeaways, giving a quick overview of the most essential considerations as AI makes its way into finance.
1. AI does not change accountability
The model delivers structure. Entities, hierarchies, mappings, consolidation rules, planning cubes. It even produces a UI with just the right shade of corporate gray. The numbers tie. The system balances. It feels like you’ve skipped a decade of implementation pain.
At this stage, AI appears not just competent, but suspiciously overqualified. It writes logic faster than your team can phrase requirements. It doesn’t complain, escalate, or invoice. It gives you the sense that complexity has been reduced to syntax.
What this means for finance leaders
Before evaluating what an AI tool can do, ask what evidence it creates.
If an auditor, regulator, or board member asks how a number was produced, can your team explain it?
2. Data quality is becoming even more important
One of the most important comments in the interview received relatively little attention.
Babington noted that organisations must be able to verify the quality of the underlying data.
That may sound obvious, but it has significant implications.
Many AI discussions focus on model capabilities. Less attention is given to the quality, consistency, and governance of the data feeding those models.
Poor data quality has always created problems in finance. AI simply accelerates the consequences.
If the underlying data is incomplete, inaccurate, or inconsistent, AI can produce convincing outputs that are still fundamentally flawed.
For finance teams, this reinforces a lesson many already know:
Garbage in, garbage out still applies.
Strong data governance remains one of the most valuable investments a finance function can make, regardless of which AI tools are adopted.
What this means for finance leaders
What this means for finance leaders
Review your data foundations before reviewing AI features.
The organisations that get the most value from AI will often be those that have already invested in clean structures, standardised processes, and clear ownership of financial data.
3. Transparency may become the next competitive advantage
One of the most interesting topics raised in the discussion involves a challenge that many organizations are not considering yet.
As AI becomes embedded in finance software, audit platforms, and reporting tools, organizations may not always know which models are being used behind the scenes.
This creates a new question:
How can you assess dependencies, risks, or potential weaknesses if you lack visibility into the technology stack supporting critical financial processes?
The interview highlights concern around correlation and evidence. If different systems are built on similar underlying models, they may share the same blind spots and reach similar conclusions.
That can create situations where outputs appear independently verified when they are not.
While standards and guidance continue to evolve, one practical step organizations can take today is to demand greater transparency from software providers.
Understanding how solutions are built, what data they rely on, and when significant changes occur will become increasingly important.
What this means for finance leaders
When evaluating technology vendors, ask more questions about transparency:
- What can the solution do?
- What drives the output?
- How is the output documented?
- How is change managed?
- What evidence is available when auditors ask questions?
Conclusion
One of the most important conclusions from the interview is that the core principles of finance have not changed.
AI may transform how work is performed, but it does not remove the need for evidence, controls, professional judgement, or accountability.
For finance leaders, the challenge is not simply adopting AI. It is adopting AI in a way that maintains trust in financial information.
The organisations that succeed will be those that can balance innovation with governance, automation with transparency, and efficiency with accountability.
Because when it comes to financial reporting, the question is not whether AI produced the answer.
The question is whether you can prove why the answer can be trusted.