Better IRS Data Will Help, Not Hurt, the American Taxpayer

In its recently released Strategic Plan for Fiscal Years 2022-2026, the IRS described how it will leverage technology and data analytics as a key component for achieving its goals within four strategic areas: service, enforcement, people, and transformation. In the plan, the IRS listed “improving data management and analytics” as one of its six focus areas for improving taxpayer experience and transforming the agency.

At first glance, the uninitiated might not recognize how access to more robust data and advanced analytics could improve the taxpayer experience at all. However, the presumption that improved data analytics will only enhance the agency’s enforcement goals is misplaced.

Although better data and analytics will certainly enhance the agency’s ability to identify noncompliance and likely increase the number of audits, these same enhanced analytical capabilities can be leveraged to improve taxpayer interactions with the IRS. The IRS having access to more and stronger data sets should not only help shrink the tax gap through improved enforcement measures but also increase voluntary compliance, support an efficient taxpayer experience, and better serve the public.

Advanced data analytics has for years been a hallmark of quality customer service at global financial institutions. For instance, think about how you correspond with your bank or other financial institutions. Interactions with the IRS should be on par with those experiences. Over the past few decades, improving data systems, increasing the quality of that data, and applying advanced analytics have become cornerstone capabilities for operational and strategic decision- making, both for the IRS and for tax administration globally.

Data-based decisions should drive where resources are deployed, whether it be to train a call site representative in a specific area, determine taxpayer needs and preferences, better understand customer behavior to help the American public voluntarily comply with the complex world of tax law, or enhance the ability to detect tax noncompliance and address that noncompliance with tailored, cost-effective treatment streams.

The strategic plan states that it will be “crucial to enhance the taxpayer experience by prioritizing data accessibility and analytical skill development to improve how we evaluate compliance.” In short, data-driven decision-making needs to enhance operational effectiveness that, in the end, will result in what many in Congress and the public have for years been asking for: a more efficient and taxpayer-first IRS.

The Office of Research, Applied Analytics, and Statistics (RAAS) is the IRS’s “centralized research and analytic organization” and has an enterprise-wide focus, led by the agency’s chief data and analytics officer.5 RAAS’s mission, as outlined by the IRS, is “to lead a data-driven culture through innovative and strategic research, analytics, statistics and technology services in partnership with internal and external stakeholders.” It advocates for data-driven agency decision-making through its collaboration with embedded research functions at the IRS. RAAS also maintains some of the government’s largest data repositories, including the Compliance Data Warehouse.

But how exactly can RAAS use improved data and advanced analytics to enhance the taxpayer experience, affect the tax gap, and increase the efficiency of IRS operations? How will data driven decision-making accomplish this? A couple of examples and use cases related to the strategic goals of enforcement and service may help to illustrate and establish the business case for the IRS to continue moving in this direction.

Over the years, data-centric teams at the IRS have used a “test-and-learn” approach to help the agency better evaluate, and ultimately resolve, enforcement actions. For instance, through its Data and Analytics Strategic Integration Board, the IRS launched an initiative to address noncompliance within the employment tax arena.

Using advanced analytics, the Service was able to identify employers that collected employment taxes from employees but failed to report the taxes to the IRS or Social Security Administration. Several criminal and civil investigations were initiated as a result of the Servicewide endeavor. The key ingredient was matching subject matter experts, or compliance employees, with data scientists to establish a “test-and-learn” environment, or iterative process, to develop systematic algorithms to effectively address noncompliance.

The IRS Large Business and International Division also leveraged this approach to develop the Large Corporate Compliance program and is now piloting a similar analytical method for large partnerships. This permitted LB&I to move toward a data-driven, risk-based approach to audit selection and away from its prior continuous audit program of large taxpayers. Thus, taxpayers with low compliance risk are no longer subject to a loop of continuous audits.

However, there is still room for improvement. Recent figures show that nearly 40 percent of audits of large corporations result in no change. For individual taxpayers, however, the rate of no- change audits has decreased from 2010 to 2021.

Reducing the no-change rate improves the taxpayer experience by ensuring that IRS resources are not erroneously focused on compliant taxpayers.

The Nationally Coordinated Investigations Unit has increasingly used data to help with case selection for the agency’s Criminal Investigation division. The new Office of Fraud Enforcement is working to use data for the same purpose by bringing together a variety of IRS operating divisions. The Office of Fraud Enforcement has stated that it intends to use data analytics to pinpoint issue areas based upon calculable risk.

Overall, enforcement actions take an immense amount of time and resources, but leveraging advanced data analytics will provide the IRS with better insight into the optimal enforcement strategy and keep compliant taxpayers out of this arena.