Can AI or Machine Learning Help Companies Combat ERTC Fraud? 

December 29, 2023
Dayes Law Firm

When the federal government offers businesses a tax credit of up to $26,000 per employee just for filing an employment tax return, it’s a given that unscrupulous individuals will take advantage of this opportunity to commit fraud. 

According to the IRS, the Employee Retention Tax Credit (ERTC), which provides a tax credit to eligible businesses, has been the target of fraud to the tune of more than $2 trillion. 

With the IRS stretched thin and thousands of already filed returns potentially being fraudulent, there’s a massive potential for AI and machine learning to step in and help prevent future fraudulent claims. Here’s how using AI for ERTC fraud prevention can be valuable for ensuring compliance with filing requirements.  

How the ERTC Attracted So Much Fraud 

Initially intended to be a financial lifeline for companies struggling due to the pandemic and economic shutdowns, the siren song of six, seven, and eight-figure tax refunds became a magnet for fraudsters. 

The most common culprit was a smooth-talking “tax specialist” who promised to claim massive tax refunds in exchange for an upfront fee and/or a hefty percentage of the funds to be paid. Even a business’s lack of eligibility was no deterrent. And, since companies have until 2025 to file an ERTC claim, these predators are not going away anytime soon. 

As a business owner, one of the best ways to gain protection against fraud is to work with a tax attorney who has extensive experience in filing these claims and a proven track record of success. If you file on your own or work with an unethical party, you are subject to repaying the funds received for which you are ineligible, and you could also face massive financial penalties. 

Using AI for ERTC Fraud Prevention 

AI (Artificial Intelligence) and machine learning can play a significant role in helping companies combat Employee Retention Tax Credit (ERTC) fraud, including fraud detection and behavioral analysis. 

Fraud Detection 

Artificial intelligence can process astronomical amounts of data in the blink of an eye, making AI adept at identifying suspicious patterns and anomalies that signify fraud. Further, by leveraging machine learning technology in data analysis, you can train AI to become even more accurate and sensitive to fraud detection. 

Behavioral Analysis 

Once AI has learned how to identify a fraudulent ERTC, machine learning algorithms can be trained to understand normal behavior patterns within the IRS’s library of tax return data. Any deviations from these patterns may trigger alerts for further investigation. 

Automation 

The IRS became so backlogged with ERTC claims in the summer of 2023, that the agency had to put a temporary halt on all claims processing. This came after detecting that there had already been more than 11,000 suspicious returns that needed additional scrutiny. 

The use of AI could help by automating compliance checks, especially in the face of ever-changing ERTC regulations. 

Examples of Using AI to Combat ERTC Fraud 

The potential applications for AI in fraud detection are practically endless, including: 

  • Flagging an ERTC claim that is excessive
  • Cross-referencing multiple data points to detect irregularities
  • Identifying fake employees
  • Automating fraud detection tasks to free humans up for other activities
  • Creating predictive models to make AI for ERTC fraud prevention more accurate 

As cyber security experts, risk management personnel, and tax professionals come together to advance tools for AI for ERTC fraud prevention applications, we can expect these systems to continue to evolve and become increasingly more accurate. 

Avoid ERTC Fraud by Working with an Experienced Tax Professional 

As a team of experienced tax professionals and attorneys, Dayes Law Firm has helped hundreds of businesses claim over $250 million in ERTC funds. Give us a call at (800) 503-2000 or fill out our online form to schedule your free consultation with our team today.