Can Machine Learning Improve Tax Enforcement?

 

Machine learning (ML), a subset of artificial intelligence, is an increasingly important technology for big data analytics which enables organizations to detect patterns and emerging trends in their operations. In 2017, the Internal Revenue Service (IRS) adopted machine learning techniques to identify fraud and other types of noncompliance by individuals claiming tax refunds. 

There are many forms of ML that can be applied to detect fraud to which they all have a different degree of success and efficiency. The main ones used in tax enforcement are:

  • Supervised Learning

    • A program in which you uncover patterns in large datasets by capturing correlations between inputs and outputs and creating an algorithm which can make predictions. 

  • Social Network Analysis 

    • Measures the strength of association between two parties. It can help authorities detect networks of actors working together to accomplish a variety of frauds such as missing traders and fake-invoice schemes.

There is a similarity between this and the early years of ecommerce. E-commerce only became widely used once consumers began to trust it, in large part because SSL encryption and security certifications gave them reassurance. Innovations in blockchain cryptography enable the security of both data and algorithms, extending trust from individuals to systems. Since the data always remains in the taxpayers' hands, tax authorities can remotely monitor taxpayer systems without having to extract or secure the data. Systems can be coordinated and then automatically kept in sync.

Combining blockchain cryptography with ML applications holds the possibility of a radically different approach to the simultaneous concerns of fraud prevention and compliance enhancement. Due to these advances, it is essential that taxpayers and tax administrations collaborate to create equitable and fair applications of blockchain, machine learning, and other quickly developing technology.

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