How Are Auditors Using AI to Review Financial Statements?

From Sampling to Full-Population Analysis

Traditional financial statement audits have often relied on statistical sampling, where auditors test a representative subset of transactions rather than reviewing every single one, largely because thoroughly examining an entire population of transactions manually would be impractical given time and resource constraints. AI-powered audit tools have changed this equation in many cases by making it feasible to analyze much larger portions of, or even entire populations of, transaction data rather than only a sample. This doesn’t mean every audit now examines 100% of transactions in every area, since the appropriate testing approach still depends on the specific audit area, risk assessment, and applicable auditing standards, but AI has expanded what’s practically achievable within typical audit timeframes.

Flagging Anomalies for Deeper Human Review

A central use of AI in auditing is anomaly detection: machine learning models analyze transaction and account data to identify entries or patterns that deviate from what’s expected, flagging them for more detailed testing by the audit team. This might include unusual journal entries made outside of normal business hours, transactions that don’t fit typical patterns for a given account, or relationships between financial statement line items that don’t align with historical trends or expectations. By directing auditors’ attention toward the areas most likely to contain meaningful errors or require closer scrutiny, AI-based flagging can help audit teams use their limited time more effectively, focusing detailed testing where it’s most likely to matter.

Natural language processing tools also play a growing role in reviewing unstructured information relevant to an audit, such as extracting and analyzing key terms from contracts, lease agreements, or other legal documents that affect specific accounting judgments, a task that previously required auditors to manually read through potentially lengthy documents to identify relevant provisions.

The Auditor’s Judgment Remains Central

Despite these tools, it’s important to understand that AI supports rather than replaces the audit process and the auditor’s professional responsibility. Forming an audit opinion, which represents the auditor’s independent assessment of whether financial statements are fairly presented, requires professional judgment that considers context, materiality, and risk in ways that go beyond what automated anomaly detection alone can determine. Regulatory bodies overseeing audit quality, including the PCAOB for U.S. public company audits, continue to hold audit firms and individual auditors accountable for the overall audit opinion regardless of which tools were used to support the underlying work.

Bottom Line

Auditors increasingly use AI to analyze larger volumes of transaction data, flag anomalies for deeper testing, and extract relevant information from supporting documents, which helps direct professional attention to higher-risk areas more efficiently, while the auditor’s independent judgment and accountability for the final audit opinion remain central to the process.

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