The explainable AI (XAI) conversation in finance runs almost entirely through one corridor: banks explaining denied loan decisions to regulators and customers. These are the frameworks that get cited, and for good reason. Credit decisions affect people’s lives, and regulators have pushed hard for traceability.

But if you run an audit team, manage accounting workflows, or sit in the office of the CFO, that conversation only partially applies to you. Your explainability question is different, and in some ways, more demanding.

Can you stand behind AI-assisted work in front of an audit committee? Can you walk an external auditor or a PCAOB inspector through an AI-generated output? When a calculated lease liability or a flagged journal entry comes from your platform, can your team trace it back to the source document it came from?

Those questions don’t get answered by credit model frameworks. They get answered by what the AI actually shows you, and whether what it shows you holds up.

This article covers what explainable AI actually means, why accounting and audit teams face a distinct standard, and what to look for when evaluating AI-powered tools.

What Is Explainable AI?

Explainable AI (XAI) refers to a set of techniques designed to make AI outputs interpretable to the people relying on them. Many AI models, particularly complex machine learning systems, can produce accurate outputs without anyone being able to fully explain the logic behind them. This is the “black box” problem. The model works, but no one can tell you exactly why.

XAI addresses this through several approaches:

  • SHAP (SHapley Additive Explanations): Quantifies which input features drove a specific result, using game theory principles to distribute credit for a prediction across the contributing variables. SHAP tells you not just what the model decided, but which factors mattered most in getting there.
  • Counterfactual explanations: Answer the question “what would have needed to change to produce a different output?” These are useful for understanding the limits of a model’s reasoning.
  • Interpretable models: Decision trees and rule-based systems built for transparency from the start, where the logic is readable without any additional explanation layer.
  • Post-hoc methods: Explanation techniques applied to complex models after the fact to generate human-readable summaries of their reasoning.

Each approach involves tradeoffs. More complex models can be more accurate but harder to explain. More interpretable models are easier to review but may sacrifice predictive precision. In most industries, XAI is about managing that tradeoff responsibly.

In accounting and audit, the stakes make that tradeoff non-negotiable.

Explainability in Finance Usually Means Banking. That’s Only Half the Story.

Most XAI literature in finance focuses on credit: banks explaining why a loan was denied, fraud detection systems flagging suspicious transactions, or insurance models pricing risk. Regulators have pushed financial institutions to document and explain their models’ outputs, and that pressure has shaped most of the public conversation.

But it addresses a fundamentally different audience than accounting and audit.

In banking, the “customer” of the explanation is often a regulator, and the central concern is fairness: did the model treat this person equitably?

In accounting and audit, the audience is the external auditor or audit committee, and the concern is professional judgment: did a qualified professional exercise appropriate judgment and document it?

That’s a different standard, and it has roots that predate XAI as a concept.

Professional auditing standards have long required auditors to understand and evaluate the methods and work they rely on. 

AU-C 620 establishes the principle that when an auditor draws on specialized work, they’re responsible for evaluating whether that work is appropriate for their purposes. PCAOB AS 1201, which governs supervision of the audit engagement, carries the same logic: the auditor owns the conclusions, and ownership requires understanding. The PCAOB’s 2025 inspection priorities formalized this further, specifically flagging the use of generative AI at public companies and broker-dealers as an area of heightened inspection emphasis.

“I ran it through the AI” has never been a complete answer in audit. It was a documentation gap before AI arrived, and it’s the same gap now, with considerably more riding on it.

What “Explainable AI” Actually Means in an Accounting Context

For accounting and audit teams, XAI breaks down into three practical layers:

Transparency 

Transparency refers to the inputs. What data did the AI use? Was the source document connected to the output? Transparency lets the reviewer understand the starting point of the AI’s work, not just its conclusion.

Interpretability 

Interpretability refers to the logic. Can a reviewer understand why the AI produced this result, not just what it produced? Interpretability is what allows a senior accountant or auditor to evaluate the AI’s reasoning and exercise professional judgment over it, rather than simply accepting the output.

Traceability 

Traceability refers to the chain of evidence. Can you reconstruct the path from input to output in a way that supports workpaper documentation? For audit, this is the most critical layer. The workpaper serves as the evidentiary record, and AI outputs need to fit into that structure to be defensible.

For the office of the CFO, this plays out in a familiar scenario. When external auditors test AI-generated financial data, the Controller needs to be able to explain the logic behind the calculations. “The platform calculated it” isn’t a sufficient answer on a provided-by-client (PBC) list or in response to a management representation request.

For audit firms, the same principle applies at the engagement level. When AI assists with substantive procedures, the senior auditor reviewing the work needs to be able to explain the basis for the conclusion. That requires understanding the AI’s reasoning, not just its result.

Trullion approaches this as a design requirement, not an afterthought. Every AI-generated calculation or finding traces back to the original contract, lease, or transaction data that produced it. That direct connection between output and source is what makes AI-assisted accounting work reviewable: a green box, not a black box.

Questions to Ask Before Trusting an AI Tool With Accounting or Audit Work

Not all AI tools are built with accounting explainability in mind. Before relying on an AI platform for accounting or audit workflows, these are the questions worth asking:

  • Can the tool show its work at the transaction or document level?
  • Is there a traceable audit trail from source document to output?
  • Can outputs be structured or exported to support workpaper documentation?
  • Does the tool flag uncertainty, low-confidence outputs, or data gaps, or does it present every result with equal confidence?
  • Has the model been independently validated? By whom, and against what standard?
  • Can the methodology be reviewed by someone without machine learning expertise, such as a senior auditor or controller?
  • Is the AI logic static and documented, or does it update in ways that aren’t visible to the user?

These aren’t abstract due-diligence questions. They’re the questions an external auditor or PCAOB inspector may ask in the course of reviewing AI-assisted audit work, and the answers need to exist before fieldwork starts, not during it.

Where the Profession Is Heading

Regulatory attention on AI in audit is accelerating across multiple fronts.

The PCAOB named AI a formal inspection priority for 2025, and its Technology Innovation Alliance (TIA) Working Group spent two years developing recommendations on how PCAOB oversight programs should address emerging technologies in auditing. The IAASB, through its October 2024 Technology Position, committed to a gap analysis of whether current standards adequately address AI and is integrating technology considerations into ongoing standard revisions rather than waiting for the field to stabilize.

At the regulatory level, the EU AI Act, which began phasing in with prohibited AI practices in February 2025, with full high-risk AI obligations for financial services expected to take effect by August 2026, classifies certain AI applications in financial services as high-risk systems subject to transparency and traceability requirements. The broader regulatory emphasis on explainability, human oversight, and documented AI reasoning reflects a direction that’s relevant well beyond EU borders.

The trajectory is consistent: explainability is moving from a differentiator to a baseline expectation for any AI used in accounting or audit workflows. Accounting teams and audit firms that build on auditable AI now are building trust with external auditors and audit committees, and getting ahead of a requirement that’s coming regardless.

Build on AI You Can Explain

The XAI conversation in finance has been dominated by banks explaining credit decisions. That matters, but it’s not the full picture for accounting and audit professionals.

The standard these teams face is stricter in a specific way: it’s professional, not just regulatory. External auditors, audit committees, and inspectors aren’t only asking whether the AI was fair. They’re asking whether a qualified professional exercised appropriate judgment and can document the basis for it.

What that requires: traceability from source to output, documentation that holds up under review, and logic a senior accountant can actually evaluate. AI tools that get this right make accounting work reviewable and auditable.

See how Trullion builds explainability into accounting workflows. Book a demo today. 

Author

Katie Cavanaugh