When AI Becomes Part of Compliance, Oversight Becomes Non-Negotiable

Artificial intelligence is no longer an emerging concept in tax compliance, it is already embedded in how financial institutions, intermediaries and corporates manage FATCA, CRS and broader regulatory obligations.

From document review and entity classification to data validation and remediation workflows, AI is increasingly woven into compliance operations. For teams under growing regulatory pressure and rising volumes, this shift is both necessary and welcome. Automation brings speed, consistency and scalability at a time when manual processes simply cannot keep up.

But as AI moves from support tool to operational backbone, an unavoidable question emerges: how do we govern systems that now influence compliance outcomes? And closely linked to that, what role should tax authorities play in shaping acceptable use?

AI Is No Longer Just “Assisting” Compliance

In many organisations today, AI systems are already:

• Reviewing onboarding and due diligence documentation
• Classifying entities and accounts
• Identifying data gaps or inconsistencies
• Prioritising remediation actions
• Producing summaries for human review and sign-off

Even where final accountability remains with people, AI is increasingly influencing how decisions are reached. That matters, because it places AI firmly inside the compliance control environment, not alongside it.

Used responsibly, AI can reduce human error, improve consistency and allow specialists to focus on judgement rather than repetition. Used without proper governance, it can also scale mistakes faster than any manual process ever could.

Why Oversight Matters More in Tax Than Almost Anywhere Else

Tax compliance is not a purely operational function. It sits at the intersection of legal interpretation, regulatory policy, data quality and cross-border reporting. Errors can result in misreporting, regulatory scrutiny, penalties and reputational damage, often across multiple jurisdictions.

Tax authorities already expect firms to:

• Understand and document their processes
• Apply appropriate controls
• Evidence decisions and outcomes
• Demonstrate accountability and governance

The introduction of AI does not dilute these expectations. If anything, it raises them. Regulators are increasingly likely to ask:

• How does this system influence decisions?
• What data does it rely on?
• How are outcomes reviewed and challenged?
• What happens when the system gets something wrong?

These are not technology questions, they are governance questions. And they are entirely consistent with existing regulatory principles.

Explainability Is a Governance Requirement, Not a Technical Nice-to-Have

One of the most persistent concerns around AI in compliance is explainability. Regulators do not need access to source code, but they do need confidence that outcomes are logical, consistent and defensible.

In tax due diligence, that means being able to explain why an entity was classified a certain way, why documentation was deemed acceptable, or why an account was flagged for further review.

Well-designed AI systems support this by:

• Logging decision factors
• Indicating confidence levels
• Producing human-readable outputs
• Maintaining clear audit trails

Poorly governed systems, by contrast, risk becoming opaque “black boxes” — and opacity is rarely compatible with regulatory scrutiny.

The Case for Clear Guidance,  Not Micro-Management

Tax authorities do not need to approve algorithms or dictate technical design. But there is a strong case for clearer, principles-based guidance on how AI should be governed when used in compliance.

Such guidance could focus on outcomes rather than implementation, clarifying expectations around:

• Human oversight of AI-assisted decisions
• Documentation and auditability
• Data quality and provenance
• Change management as models evolve
• Accountability across jurisdictions

This kind of framework would give firms greater confidence to innovate responsibly, rather than second-guessing what may be challenged in an audit or enquiry.

Is Accreditation a Step Too Far, or a Logical Next One?

The idea of tax authorities offering voluntary accreditation for AI-enabled compliance solutions may sound ambitious, but it is not unprecedented. Other regulated domains already rely on certification models, from payment systems and data protection to model validation in financial risk.

An accreditation framework would not restrict firms to a narrow set of tools. Instead, it could:

• Signal that solutions meet baseline governance and transparency standards
• Reduce duplication of assurance across institutions
• Encourage vendors to design with regulatory expectations in mind from the outset

For firms, this could mean stronger audit defensibility. For authorities, better visibility into how AI is shaping compliance practices across the industry.

Getting the Balance Right

There are, of course, risks in over-regulation. AI evolves quickly, while tax regulation moves more deliberately. Prescriptive rules could unintentionally stifle innovation or lock the industry into outdated approaches.

That is why any oversight framework must be principles-based, proportionate to risk, and flexible enough to evolve. The goal is not to regulate AI itself, but to regulate how it is used in compliance.

Human Accountability Still Sits at the Centre

One point is non-negotiable, AI does not remove the need for human expertise. In FATCA, CRS and similar regimes, judgement remains essential. AI can assist, prioritise and surface risk, but accountability ultimately sits with people.

In practice, AI often makes human oversight more important, not less. Someone must understand where models may struggle, challenge unexpected outcomes, and ensure systems evolve alongside regulatory change. Tax authorities will expect this, and rightly so.

AI is now a permanent feature of the tax compliance landscape. The question is no longer whether it will be used, but how responsibly it will be governed.

As adoption accelerates through 2026 and beyond, there is an opportunity for tax authorities to move from passive observers to active shapers of acceptable practice. Clear guidance, thoughtful oversight and even voluntary accreditation could help ensure AI strengthens compliance rather than undermines it.

And if that occasionally means asking how the machine works, that is not a cause for concern, it is a sign that compliance, like the technology supporting it, is maturing.

When compliance gets smarter, everyone benefits.

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