Key Details: The IRS is planning to operationally integrate artificial intelligence (AI) and machine learning into its audit selection process, marking a major shift from traditional methods. Historically, the IRS relied on older statistical models and manual review, which often led to high “no-change” audit rates—audits that found no additional tax owed. With an estimated $688 billion annual Tax Gap, the agency is under pressure to make enforcement more effective. The Treasury Inspector General for Tax Administration (TIGTA) reported that while these models show promise, the IRS needs stronger evaluation processes, feedback loops, and ensemble learning techniques to maximize performance. High-income individuals, large partnerships, and sizable corporations may face heightened scrutiny as AI reshapes the audit selection process.
The IRS is now deploying AI models across multiple taxpayer segments, shifting from broad statistical scoring to more sophisticated, relationship-driven analysis. Each model is designed to identify high-risk returns with greater precision, reduce wasted audits, and direct resources where they matter most.
For individual taxpayers, the IRS has introduced a machine-learning (ML) classification model that automatically analyzes each return and recommends the top three issues most likely to require adjustment. The IRS has proactively been integrating these ML models prior to 2020, even before the Presidental EO: “Promoting the Use of Trustworthy AI in the Federal Government”.
For corporations with assets between $10 million and $250 million, the IRS has replaced the outdated Discriminant Analysis System (DAS) with the new Line Anomaly Recommender (LAR). Unlike its predecessor, LAR looks at the relationships among line items—such as income, deductions, and credits—rather than flagging isolated anomalies. Early testing shows lower “no-change” audit rates and better coverage, as the model evaluates the entire return population instead of relying on limited samples.
Large, complex partnerships such as hedge funds, private equity, and real estate investment groups have historically been difficult for the IRS to audit effectively. To address this, the IRS developed the Large Partnership Compliance (LPC) model, which applies machine learning to the entire population of large partnership returns and then incorporates expert review by tax specialists. This is the IRS’s first effective enforcement tool for this taxpayer segment. In Tax Year 2021, the LPC flagged 82 high-risk partnership returns for examination, compared to near-zero audits in prior years.
The IRS’s AI models are trained to detect patterns and inconsistencies that may indicate noncompliance. Common red flags include:
While wealthy individuals, large partnerships, and mid-size corporations remain key enforcement priorities, recent reports show that IRS AI audits are also expanding to a wider range of taxpayers. According to industry analysis, the IRS’s algorithms may or may not also be targeting:
This means that freelancers, gig workers, and taxpayers who rely on DIY tax software may also face greater scrutiny. Even small discrepancies or honest mistakes—such as forgetting a side income 1099 or misreporting digital asset transactions—can trigger an automated audit flag.
The adoption of AI promises several benefits for both the IRS and taxpayers but also raises challenges that need to be addressed.
For taxpayers, the message is clear: the IRS’s use of AI means greater scrutiny for high-income individuals, large partnerships, and corporations with complex financial structures. Wealthy taxpayers who report low taxable income despite significant assets are likely to face closer examination, as are entities with multi-tiered partnership structures or unusual financial ratios. In this environment, documentation, accurate reporting, and consistency across related returns are more critical than ever. Businesses and individuals alike should take a proactive approach by reinforcing internal controls, engaging in tax planning, and maintaining audit readiness to mitigate the risk of costly disputes.
The IRS’s adoption of AI is not a short-term initiative—it represents a permanent shift in enforcement strategy aimed at closing the Tax Gap and making audits more targeted. Businesses and high-income individuals should not wait for an audit notice to review their compliance practices. Instead, now is the time to take proactive steps to strengthen documentation, improve reporting accuracy, and prepare for heightened scrutiny.
To help minimize audit risks in this new environment, taxpayers are encouraged to:
At Ryan & Wetmore, our advisors can help you assess compliance risks, implement stronger recordkeeping processes, and develop an audit-readiness strategy tailored to your unique situation. Contact us today to discuss how AI-driven audit selection may impact your business or personal tax profile.
About Rosie Cheng
Senior Finance Consultant
Rosie Cheng is a Senior Finance Consultant at Ryan & Wetmore. She focuses on government contracting services and produces many of the firm’s government contracting newsletters. Rosie earned her Master of Science in Management from Georgetown University and a BBA from William and Mary.