Artificial intelligence is now an operational part of the Internal Revenue Service’s audit and examination process. The agency has moved from traditional risk scoring models to machine learning and data analytics, aiming to improve its ability to identify high-risk cases and reduce unnecessary audits.
The IRS previously relied on legacy systems that flagged known issues but often missed complex structures and cross-entity transactions. With AI, the agency analyzes patterns across tax returns, forms, payments, and third-party data. This approach is expected to enhance accuracy in audit selection.
According to Mark G. McCreary, partner and chief artificial intelligence & information security officer at Fox Rothschild LLP, “Expect a sharper focus on high-risk cases, especially those with layered entities, unusual loss positions, or mismatched information returns. At the same time, there should be fewer no-change audits. Better selection increases the chance that an audit results in an adjustment. There is a strong argument that this is good policy. It is also a cue to businesses. If you are selected, the IRS likely sees something specific. You should too.”
AI tools will provide agents with real-time risk indicators during examinations, allowing for more targeted reviews and faster case resolution. The use of these technologies means documentation quality will become increasingly important for taxpayers.
McCreary advises: “Start with alignment. Reconcile your story across the return, financials, and footnotes. Inconsistencies are easy for machines to spot. They are harder to explain after selection. Tie key tax positions to contemporaneous memos. Cite the specific code sections and facts relied upon. Keep those memos in a ready file.”
He also highlights common risk signals: “Clean your data. Many risk signals arise from simple errors. Mismatched names or EINs. Round-dollar entries that do not reconcile. Missing attachments. Incomplete partner or shareholder information.” He suggests running prefiling checks similar to what IRS algorithms might perform.
Sensitive areas such as transfer pricing and R&D claims require thorough documentation due to their complexity and potential scrutiny by AI models: “Document transfer pricing, valuation, and R&D claims with care…Keep working papers that link to the filed return and to invoices or contracts,” McCreary notes.
Businesses are encouraged to anticipate questions by providing clear explanations for new structures or significant changes within their filings: “If you have a new structure, a large NOL, or a change in method, draft a short explanation statement.”
Coordination between business owners and advisors before filing can help streamline responses if selected for examination: “Coordinate with your advisors early…If you face an exam, the first 30 days matter.”
While AI helps address staffing shortages at the IRS by triaging workload and routing complex cases efficiently, McCreary points out that human oversight remains central: “Agents make determinations. But tools will shape focus.” He recommends asking early about audit scope if selected.
There are concerns about fairness in AI-driven selections; McCreary says bias monitoring is necessary: “Bias and fairness need testing…Expect the IRS to keep refining controls.”
Data privacy measures are also being strengthened as analytics expand within the agency’s operations: “Privacy-by-design is advancing…Safeguards, role-based access, and audit trails are essential.”
For business owners whose personal returns connect closely with business filings—especially involving flow-through entities—AI may more easily identify discrepancies requiring review.
McCreary concludes: “AI at the IRS is here to stay…Treat your return like it will be read by a machine first and a human next.”
