Turning Standards into Rules Part 4: Machine Learning and Economic Substance
(2018) 249 DTR 11 (Bloomberg BNA)
3 Pages Posted: 22 May 2019
Date Written: December 27, 2018
The federal tax code states that in order for a transaction to be recognized for tax purposes, it must pass a two-pronged "economic substance" test (Section 7701(o)(1)). If a transaction has no purpose or effect beyond generating tax savings for the parties involved, it lacks economic substance. The Internal Revenue Service will collect the taxes as though the transaction hadn’t occurred and will impose a penalty of 20 percent of the disallowed benefit. This penalty may be increased to 40 percent in cases of blatant fraud (Sections 6662(b)(6), 6662(i)(1-2)). Although the two-pronged test outlined in the statute provides general guidance, courts primarily use a multi-factor common law test when trying to determine whether the economic substance doctrine applies to a particular transaction. For our machine learning system, we selected the factors most commonly mentioned in the leading cases, as well as many of those listed in the IRS 2011 directive on the economic substance (‘‘Guidance for Examiners and Managers on the Codified Economic Substance Doctrine and Related Penalties’’). When the system was calibrated and tested, we found that some factors had more influence on outcomes than others. In the past, tax advisors had to rely on their intuition to determine how judges might assign weights to different factors and how these factors might be applied in future cases. This analysis reveals that by applying machine learning, we can augment professional intuition with concrete predictions.
Keywords: machine-learning, economic substance, tax avoidance
JEL Classification: H2, H20, H29
Suggested Citation: Suggested Citation