Canada: Using machine learning to crack the tax code
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In this first installment of Blue J Predicts on Tax Notes for 2022, we take stock of the inaugural year of this column of 2021. We review our predictions from the past year and reflect on the state of technology for tax forecast. 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 covered cases on topics such as:
- The doctrine of economic substance
- Innocent Spouse Relief
- Classification of workers
- Captive Insurance Plans
- The deductibility of ordinary and necessary expenses
- The Doctrine of Income Attribution
- Good faith partnerships
Each episode of our column discussed various predictions about the outcome of a case and the insights our algorithms were able to generate on relevant tax issues by leveraging ML.
In this article, we reflect on the predictions we’ve made over the past year and provide general observations on how tax practitioners are beginning to learn how to leverage ML insights to “crack the code.” We also look at how practitioners use BC to quantify risk to their clients and ensure that tax advice can properly withstand the scrutiny of the IRS and the courts. The goal is to guide tax professionals in their tax planning and help them design the most effective ways to resolve tax disputes, using new tools and technologies.
We start with an overview of artificial intelligence (AI) and discuss what exactly machine learning is. We then explain how reliable machine learning models are specifically designed for tax and highlight 4 concrete benefits of using machine learning in a tax practice today:
- Quantify the risk
- Optimizing the commercial/fiscal strategy
- Discover the blind spots
- Optimizing litigation strategy
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