Tax agencies at both the state and federal levels are under increasing pressure to close the growing tax gap, which is the difference between taxes owed and taxes paid. Traditional methods of identifying noncompliance in sales tax reporting have proven time-consuming, inefficient, and prone to errors. Now, thanks to advanced technologies like artificial intelligence (AI) and big data analytics, tax authorities are finding new, more efficient ways to combat tax evasion and increase revenue collection.

In this blog post, we’ll explore how leveraging AI and big data is helping government agencies streamline tax enforcement, increase voluntary compliance, and recover billions of dollars in uncollected sales tax.

What is the Tax Gap?

The Internal Revenue Service (IRS) estimates that the national tax gap—the difference between what taxpayers owe and what they actually pay—stands at around $406 billion. A significant portion of this comes from underreported sales tax, which leads to further underreporting of income tax, particularly for small businesses.

Small businesses, especially those with weak internal controls, are more likely to misreport sales tax, either intentionally or unintentionally. Research shows that businesses with fewer employees are more prone to sales tax noncompliance, making it a major focus area for tax authorities.

For example, in Florida alone, businesses with fewer than five employees collected more than $2.6 billion in sales tax, while businesses with fewer than 20 employees collected over $6 billion. The potential for sales tax evasion in this segment is substantial, making it a prime target for revenue agencies looking to close the tax gap.

Traditional Methods vs. Advanced Analytics

Historically, tax enforcement has relied heavily on manual processes, including audits and on-site inspections, which are time-consuming and resource-intensive. These methods are not only slow, but they often result in a high rate of false positives, where businesses are wrongly flagged for noncompliance.

Enter AI and big data analytics.

With AI, machine learning algorithms, and advanced data analytics, tax agencies can now automate much of the compliance identification process. This new approach reduces the need for manual audits, minimizes human error, and accelerates the process of identifying fraudulent or noncompliant businesses.

By leveraging AI, tax agencies can now analyze vast amounts of data in real-time, predict noncompliance behaviors, and make data-driven decisions faster than ever before.

The nForce Solution: Harnessing AI to Combat Sales Tax Noncompliance

At nForce, we’ve developed a platform designed to help revenue agencies maximize their performance by integrating AI and big data analytics into their tax enforcement processes. Our system applies advanced machine learning algorithms and AI to analyze high volumes of sales tax filings and business registrations, identifying noncompliance more accurately and efficiently than traditional methods.

Key Benefits of the nForce Platform:

• Automated Detection: Machine learning algorithms automatically detect fraud and noncompliance, reducing the need for manual audits and minimizing false positives.
• Real-time Data Analysis: Our system processes vast amounts of data in real-time, ensuring that tax agencies can respond to noncompliance issues quickly.
• Resource Efficiency: The platform reduces the number of costly on-site audits, often resolving issues through simple letter campaigns—saving time and resources for tax authorities.

Studies show that 70% of businesses that receive a letter about noncompliance pay their outstanding sales tax without any additional legal action.

Case Study: Real Results from Big Data Analytics

To illustrate the effectiveness of the nForce platform, let’s look at a recent pilot project conducted by our team. We analyzed 36 months of sales tax filings and business registrations from two specific NAICS codes—mail-order businesses and convenience stores without gas stations.

Using the scalable power of AWS GovCloud services, including EC2, S3, Athena, and SageMaker, we applied machine learning algorithms to over 1.3 million records. We segmented businesses by type and size, then used AI-driven clustering techniques to predict similar behaviors within those segments. This allowed us to pinpoint noncompliant businesses with incredible accuracy.

The results were impressive:

• We identified $42 million in uncollected sales tax over a 36-month period.
• Our analysis was completed in just 90 days—an effort that would have taken the state Department of Revenue years to accomplish manually.
• The noncompliance included delinquent reporting, underreported cash sales, and improperly claimed exemptions.

In addition to identifying uncollected tax dollars, the project also uncovered significant data integrity issues, such as businesses misclassified under incorrect NAICS codes. By addressing these data challenges, the nForce platform helps improve overall compliance and enhances the accuracy of tax reporting.

Closing the Tax Gap with Advanced Analytics

The results of our pilot program show how advanced analytics and AI can transform tax enforcement, making it faster, more efficient, and more accurate. With minimal additional resources, tax agencies can harness the power of big data to identify and recover uncollected revenue, while also driving voluntary compliance from businesses.

By adopting technologies like nForce, tax authorities can significantly reduce the tax gap, ensuring that essential public services have the funding they need to operate effectively.

Ready to Learn More?

If you’re interested in seeing how nForce can help your agency combat sales tax noncompliance and recover uncollected revenue, get in touch with us today to schedule a demo.