Using Machine Learning to Crack the Tax Code

In this first installment of Blue J Predicts on Tax Notes for 2022, we take stock of this column’s inaugural year of 2021. We review our predictions from the past year and reflect on the state of the technology of tax prediction. In our 2021 articles, we applied machine learning (ML) algorithms to analyze the likely outcomes of pending or recently decided federal income tax cases on an assortment of tax issues. Over the past year, our columns have addressed cases on topics such as:

  • The economic substance doctrine
  • Innocent spouse relief
  • Worker classification
  • Captive insurance arrangements
  • The deductibility of ordinary and necessary expenses
  • The assignment of income doctrine
  • Bona fide partnerships

Each installment of our column discussed various predictions on the outcome of a case and the insights that our algorithms were able to generate on the relevant tax issues by leveraging ML.

In this article, we reflect on the predictions we made over the past year and provide general observations about how tax practitioners are beginning to learn how to leverage the insights of ML to “crack the code.” We also examine how practitioners use ML to quantify risks for their clients and ensure that tax advice can properly withstand scrutiny from the IRS and the courts. The goal is to guide tax experts in their tax planning and to help them devise the most effective ways to resolve tax disputes, leveraging new tools and technologies.

We begin with a background on artificial intelligence (AI) and discuss what exactly machine learning is. We then walk through how reliable machine learning models are specifically built for tax and highlight the 4 real-world advantages of using machine learning in a tax practice today:

  • Quantify risk
  • Optimize business/tax strategy
  • Uncover blind spots
  • Optimize litigation strategy

To read the complete article, follow the link below.

Read the full article on Tax Notes

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