What does it mean to “assure AI” in finance?
KPMG answers the question finance leaders must resolve in May 2026: what does it mean to assure AI when AI is helping produce financial information? KPMG’ discusses how assurance must now extend beyond financial statements to the AI systems, data, and controls that shape the information those statements rely on.
What does it mean to “assure AI” in finance?
This question is front and center because finance leaders are no longer experimenting at the edges of AI—they are embedding it into core financial workflows. In the next 18 months, 93% of US companies expect to be deploying or scaling AI in their finance functions, and half are already planning to orchestrate or develop multi‑agent AI systems across those workflows.
As AI shifts from adoption to orchestration, it increasingly influences the accuracy, completeness, and reliability of financial outputs. When AI systems help generate forecasts, classifications, and judgments that feed financial reporting, the question is no longer whether AI should be trusted—it is how that trust is earned and validated.
Why It’s More Complex Than It Looks
Assuring AI is more complex than assuring traditional systems because AI does not behave like deterministic software. Models learn, adapt, and interact with data in ways that are often opaque, even to their designers. As organizations move toward orchestrated and multi‑agent environments, risks multiply across data integrity, model performance, cyber exposure, and governance.
At the same time, leaders face pressure to move quickly. AI promises faster, predictive insights, but speed without confidence can undermine trust in the very information decision‑makers rely on. The tension between acceleration and assurance is now central to the finance function’s evolution.
The Evidence
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KPMG’s Answer
Assuring AI means expanding the scope of assurance from outcomes to systems.
Financial statement assurance alone is no longer sufficient when AI systems help generate the information those statements are built on. Assurance must address the integrity of data inputs, the reliability and performance of models, and the controls governing how AI is deployed and monitored.
This represents a shift in the role of the auditor. To maintain trust in the capital markets, the auditor of the future will need to provide assurance not only over reported numbers, but over the AI systems that influence them. That includes evaluating whether AI models operate as intended, whether risks are identified and managed, and whether governance keeps pace with increasingly sophisticated use cases.
Organizations that fail to extend assurance in this way risk slowing innovation rather than enabling it. Without confidence in AI‑generated outputs, leaders hesitate, regulators scrutinize, and th promise of AI‑driven decision advantage stalls.
Treat AI assurance as a strategic enabler, not a defensive check. As AI becomes embedded in finance, independent validation provides the confidence leaders need to move faster—not slower.
Assess whether your assurance model matches your AI maturity. If AI systems influence financial reporting or decision‑making, assurance must extend to data, models, and controls, not just end results.
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