Researching federal income tax issues demands distilling the law from the code, regulations, revenue rulings, administrative guidance, and sometimes hundreds of tax cases that may all be relevant to a particular situation. When a judicial doctrine has been developed over many decades and applied in many different types of cases, the case-based part of this research can be particularly time consuming. Despite an attorney’s best efforts, uncertainty often remains regarding how courts will decide a new set of facts, as previously decided cases are often distinguished and the exercise of judicial discretion can at times lead to surprises. To minimize surprises as well as the time and effort involved in generating tax advice, Blue J’s machine-learning modules allow tax practitioners to assess the likely outcome of a case if it were to go to court based on the analysis of data from previous decisions using machine learning. Blue J also identifies cases with similar facts, permitting more efficient research.
In previous installments of Blue J Predicts, we examined the strengths and weaknesses of ongoing or recently decided appellate cases, yielding machine-learning-generated insights about the law and predicting the outcomes of cases. In this month’s column, we look at a Tax Court case that our predictor suggests was correctly decided (with more than 95 percent confidence). The Ryder case1 has received significant attention from the tax community. It involved tax avoidance schemes marketed by the law firm Ernest S. Ryder & Associates Inc. (R&A) that produced more than $31 million in revenue between 2003 and 2011 and for which the firm reported zero taxable income. The IRS unmasked more than 1,000 corporate entities that R&A’s owner, Ernest S. Ryder, had created and into which he funneled the money. By exposing the functions that these entities performed, the IRS played the most difficult role in the case. Yet, there are deeper lessons that can be drawn from the litigation by subjecting it to analysis using machine learning.
In this installment of Blue J Predicts, we shine an algorithmic spotlight on the legal factors that determine the outcomes of assignment of income cases such as Ryder. For Ryder, the time for filing an appeal has elapsed and the matter is settled. Thus, we use it to examine the various factors that courts look to in this area and to show the effect those factors have in assignment of income cases. Equipped with our machine-learning module, we are able to highlight the fine line between legitimate tax planning and illegitimate tax avoidance in the context of the assignment of income doctrine.
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