AI in Financial Reporting: Where It Helps, and Where It Creates Risk

Published June 2026

Artificial intelligence is rapidly becoming part of the financial reporting process. Finance teams are using AI tools to analyze large datasets, identify anomalies, summarize disclosures, draft narratives, and accelerate routine reporting tasks.

The potential benefits are significant. AI can reduce manual effort, improve efficiency, and help accounting teams manage increasing reporting demands with limited resources.

At the same time, AI introduces new risks that organizations cannot afford to overlook. Financial reporting requires accuracy, consistency, documentation, and professional judgment—areas where AI can still fall short.

For CPAs and finance professionals, the challenge is no longer whether AI will be used in financial reporting. The challenge is understanding where it adds value, where it introduces risk, and how to implement appropriate oversight.

How AI Is Being Used in Financial Reporting

Many organizations are already incorporating AI into portions of the reporting process, including:

• Drafting MD&A and financial statement narratives
• Reviewing large volumes of transactions for unusual activity
• Identifying trends and variances in financial data
• Supporting account reconciliations
• Assisting with disclosure preparation
• Analyzing internal control documentation
• Summarizing accounting guidance and technical research

These applications can help finance teams complete routine work more efficiently and focus attention on higher-value analysis and decision-making.

Where AI Delivers Meaningful Benefits

Faster Data Analysis

One of AI's strongest capabilities is processing large amounts of information quickly.

Rather than manually reviewing thousands of transactions, accounting teams can use AI tools to:

• Identify unusual trends
• Flag potential exceptions
• Surface outliers for further review
• Highlight changes from prior periods

This allows professionals to focus their attention where risks are most likely to exist.

Improved Reporting Efficiency

Reporting timelines continue to tighten. AI can assist with:

• Data aggregation
• Initial draft preparation
• Disclosure comparisons
• Document summarization
• Workflow automation

These efficiencies can help organizations accelerate month-end and quarter-end close processes without proportionally increasing staff resources.

Enhanced Risk Identification

AI can recognize patterns that may not be immediately visible through manual review. Organizations are increasingly using AI-powered tools to identify:

• Potential control weaknesses
• Unusual journal entries
• Emerging compliance issues
• Transaction anomalies

When paired with human review, these capabilities can strengthen financial reporting oversight.

Support for Technical Research

AI tools can quickly summarize complex accounting topics and identify potentially relevant guidance. For accounting professionals, this can shorten the time spent researching:

• New accounting standards
• Disclosure requirements
• Industry-specific reporting considerations
• Regulatory developments

However, summaries should always be verified against authoritative sources before conclusions are reached.

Where AI Creates Financial Reporting Risk

Despite its advantages, AI introduces several risks that finance teams must actively manage.

Hallucinated Accounting Guidance

One of the most widely discussed AI risks is the generation of information that appears credible but is incorrect.

Examples include:

• Invented citations
• Misquoted accounting standards
• Incorrect interpretations of guidance
• Nonexistent regulatory requirements

Because these outputs often sound authoritative, they can be difficult to identify without technical review.

Lack of Professional Judgment

Financial reporting frequently requires judgment based on facts and circumstances.

Examples include:

• Materiality assessments
• Revenue recognition decisions
• Fair value estimates
• Impairment analyses
• Disclosure determinations

AI can analyze patterns, but it cannot fully evaluate business context, management intent, or stakeholder expectations.

Professional judgment remains essential.

Inconsistent Results

Generative AI models do not always produce identical outputs when asked similar questions. This inconsistency creates challenges for:

• Reporting standardization
• Documentation
• Audit support
• Internal controls

Organizations must establish processes to ensure outputs remain reliable and repeatable.

Data Privacy and Confidentiality Concerns

Many AI tools require users to input information for analysis.

Without proper controls, organizations risk exposing:

• Financial information
• Client data
• Proprietary business information
• Confidential reporting materials

Finance leaders should understand exactly how AI platforms handle, store, and process data before incorporating them into reporting workflows.

Control Environment Challenges

Organizations increasingly face questions such as:

• Who reviews AI-generated outputs?
• How is accuracy validated?
• What documentation is retained?
• How are AI-related risks assessed?
• What governance policies are in place?

These questions are becoming increasingly important as auditors, regulators, and boards evaluate AI adoption within finance functions.

What Strong AI Governance Looks Like

Organizations that use AI effectively typically combine technology adoption with strong governance practices. Important elements include:

• Human review of all material outputs
• Documentation of AI-assisted processes
• Clear approval and accountability structures
• Data privacy safeguards
• Ongoing employee training
• Periodic testing and validation of AI-generated results

The goal is not to eliminate AI from financial reporting. The goal is to ensure that efficiency gains do not come at the expense of accuracy or compliance.

The Growing Regulatory Conversation

Regulators and standard setters continue to evaluate AI's role in financial reporting, internal controls, and corporate governance. Questions surrounding:

• Disclosure expectations
• Internal control implications
• Audit procedures
• Model governance
• Accountability frameworks

are likely to become more prominent in the coming years.

Organizations that establish thoughtful governance now will be better positioned as expectations evolve.

Finding the Right Balance

AI can help finance teams work faster and uncover insights that might otherwise be missed. But financial reporting remains a high-stakes function where accuracy, transparency, and professional judgment are non-negotiable.

The most effective organizations are treating AI as a powerful support tool—not a substitute for accounting expertise. By combining automation with strong controls, technical knowledge, and professional skepticism, CPAs can capture the benefits of AI while managing the risks that accompany it.

CPE Inc. offers courses covering AI, financial reporting, accounting standards, auditing, and internal controls to help professionals stay current as technology continues to reshape the profession. Explore our upcoming webinars and self-study courses to deepen your expertise and earn CPE credit.