The digitalization of the economy and the acceleration of powerful artificial intelligence (AI) tools have necessitated that businesses and firms adapt with their own automated processes to aggregate large swaths of data across different levels of tax jurisdictions, according to a tax professional.
Digital Shift
Modern tax administration has shifted towards an increasingly digital ecosystem, a change accelerated by the COVID-19 pandemic, which led to a massive backlog in unprocessed paper filings. Mailed returns, checks, and correspondence take longer to process and integrate into computer systems, an issue flagged during the pandemic frequently by the National Taxpayer Advocate and other watchdog agencies.
Speaking with Checkpoint, Avalara Vice President of U.S. Tax Policy Scott Peterson discussed his experience with federal, state, and local governments at the time. “We sent them checks … and they would sit there for weeks and weeks and weeks.”
While tax administration at its core is the largely the same, Peterson said “one of the positive things” that has come out of the pandemic is the departure from many analog functions in favor of automation. “[P]eople realized that life has to go on,” but also, “people have to be employed,” he added. A major challenge now for the tax community and for the IRS is how to handle the immense volume of data they now receive.
AI in Enforcement
To manage this data deluge, both tax authorities and private firms are turning to AI and machine learning. These tools are critical for reconciling information from a multitude of sources. Peterson gave an example of a large company that had to pull data from 70 different accounting systems just to file one tax return.
Peterson said the same issue plagues the IRS and large accounting firms, which receive data in thousands of different formats. The problem then becomes whether the data is accurate. “If you’re an accountant out there, you have to assume that your client’s data is correct,” Peterson stated. But he offered that the right AI tools can help “go out and look for patterns” and structure the data in a way that is reliable.
This pattern-recognition capability is also what drives modern audit selection. AI can analyze vast datasets to determine what is common for a particular income type, business class, or industry, and then flag outliers for review. Peterson explained the process: “You look for things that are common, and you set them aside. And you look for the things that don’t look common.” This allows agencies to focus their attention on returns that deviate from the norm.
He pointed to examples in Minnesota and Alabama, where wholesalers are required to submit sales reports. These operations are particularly challenging for smaller entities with fewer resources at their disposes. As Peterson observed, “every sales tax nuance that exists in United States is inside” a typical “convenience store, and it’s really easy to make a mistake.”
Computer-Powered Enforcement
While the concepts behind AI and what was once called “machine learning” have been around for decades, a game-changer is the rapid uptick in computing power. Peterson described the advent of “enormous data centers” that have “the ability to run a million transactions through a system within a very short period of time.” For decades, the IRS had more data than it could use, but now it has the capacity to analyze much of it practically in real time, he illustrated.
In Peterson’s view, this opens the door to a more effective tax system. In an ideal world, transactional data would be reported continuously, not just annually. “There really isn’t any reason why you can’t get … a weekly or a bi-weekly record of me,” he commented, describing a system where withholding, 1099s, and other financial data create an ongoing analysis of economic activity. This would, theoretically, give the IRS insight toward how to dramatically improve its ability to enforce tax laws, particularly in the hard-to-track cash economy.
While such a system is not yet a reality, the technology to build it now exists, perhaps signaling a future where advanced data analytics will be a fixture of tax enforcement at a modernized IRS.
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