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Finally, recent advanced in machine learning have enabled training to be performed across multiple datasets without having to co-locate the data. The concept is known as “federated learning” and allows multiple parties to collaborate and build machine learning algorithms without having to expose their data to each other. In the future joint data contributions, from taxpayers and tax administrators alike, might be used to create algorithms. Once deemed sufficiently accurate, it might be possible for tax administrations to provide these algorithms to taxpayers either on the cloud or as a download. With federated learning and the ability to download the resulting shared algorithm, all taxpayer data can remain in the control of the taxpayer even when receiving the benefits of machine learning.

In summary, while we have only highlighted a few, there are dozens of machine learning techniques that could be applied to tax compliance and fraud detection. But as an exponential technology much of what is written here has likely been superseded with new advances by the time you read this. Suffice it to say that machine learning, in addition to traditional rules-oriented systems, will each play a significant role tax risk assessment and fraud detection moving forward. These powerful approaches are now available to tax administrations. Further, through federated learning honest taxpayers and tax administrations can now jointly contribute and benefit from machine learning algorithms without sacrificing privacy. The opportunity for cooperation has never been clearer. But what stands in the way is trust.