Big Data and Tax Reform

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The term “big data” is often associated with the internet. Google, the world’s largest and most popular search engine, famously collects data from its many millions of users, instantaneously connecting them with personalized results based on their geographic location, prior searches, and online navigation. Social media sites like Facebook leverage user data to customize advertisements, to optimize news feeds and friend suggestions to each individual with an account [1]. Netflix has helped revolutionize the entertainment industry by using big data to create targeted suggestions, with customized lists of titles, movie stars, and television shows for each of its subscribers.

Big data brings the power of computers together with some of the biggest inventories of hyper-individualized information to give us insight into every aspect of modern life and human behavior. Naturally, data scientists, policymakers, and tax experts are interested in using the same sort of big data mechanisms to advance the study, and reform, of taxation [2]. As America’s leaders continue to propose and debate comprehensive tax reform plans, they are gaining increasing access to big data analytics to inform their policies, priorities, and strategies.

The Power of Big Data Analytics in Taxation

The scale and detail of data gathered by services like Google and Facebook depends in part on their user base. Both have become nearly ubiquitous tools of modern life, which means they can gather and organize data about a huge majority of the population [1]. Something similar can be said for tax data: nearly every American citizen and corporate entity is responsible for paying and reporting tax information. That means a huge amount of data is available for collection, organization, management, and analysis. In other words, the very definition of big data [2].

Of course, tax records and tax analysis are nothing new; such functions have been around about as long as tax collection. What big data means for tax analysis is a matter of increased scale, precision, and enhanced computational potential, and using this new technology to inform the evolution of tax policy. The power and sophistication of modern computers means every individual, organization, or governmental department can become more efficient and deliberate in how they examine and process tax data, integrating computers and data science more than ever before [2].

So what does all this broad potential for using big data to support comprehensive tax reform mean in practical terms?

Specifically, data scientists and tax experts are working together to use tax data for two new functions: providing insight, and foresight [3].

Insight, in this case, means looking more closely and forensically at the data that has already been gathered, and organizing it so that more complex queries can extract new understanding and visualizations from existing data. Foresight, meanwhile, means looking at tax data not just to understand the past, but to predict the future with unprecedented detail and accuracy [3].

Predictive Analytics and Tax Compliance

Foresight empowered by big data is generally called predictive analytics, and tax experts are already busy putting predictive analytics to work in tax collection and compliance enforcement.

Traditionally, and even currently in many cases, tax compliance has been backward-facing. When it comes to enforcement, the primary mechanism used by the IRS has historically been audits [4]. These time-consuming investigations focus on a forensic review of tax records and an individual or organization’s financial records to look for inconsistencies, errors, or criminal tax avoidance [4]. They cost the government a great deal of money, and provide extremely modest returns in terms of recovered revenue.

The great power of tax data analytics in the big data era, is that it can be used predictively both by individuals and taxpaying organizations, as well as the IRS and collections or enforcement authorities [3]. Having people and machines managing all this data, processing it, and analyzing it, can produce trend data that extends forward, and creates scientifically and statistically robust predictions of what tax revenues and behavior ought to look like, before a penny is actually collected or a single 1040 return is submitted [2]. Using historical data to create detailed models for the future, both payers and collectors can immediately review returns to find errors, and to explain variations or significant changes, effectively pre-empting much of the audit process. Using these computer models, tax collections could be at least partially automated based on these predictive analytics, reducing the cost of audits to both the government and taxpayers [2].

This has obvious implications not only for compliance and enforcement, but for developing tax policy and tax reform. More precise expectations for revenues can be set based on predictive models, adjusted to reflect possible changes to the tax code. Compliance could be better enforced through more scrutiny of predicted returns, compared against actual returns, down to the individual or corporate entity, without requiring a significant increase in IRS staffing or manual enforcement activities [2].

Naturally, when data science is used on both sides of the tax equation — by payers as well as collections authorities — it becomes possible to begin automating more of the process. In theory, automating taxation could save money by reducing the overhead of the IRS, as well as cutting down significantly on the time taxpayers (individuals or corporations) must commit to tax planning and reporting. For all the potential benefits, however, it is first necessary to achieve data management and reporting standards. To take advantage of big data, it has to be shareable [5].

Big Data Sharing and Comprehensive Tax Reform

This is also a priority that data scientists and tax experts are working to address.

Part of what makes big data powerful is not just the scale of the information being managed, but how it is being managed. Because big data, by definition, comprises massive amounts of information, it requires sophisticated computers running highly structured algorithms to make sense of it all, and turn the raw data into useful insights. Making this data coherent to computers, and compatible with these algorithms, requires standardization of structure, storage, and organization. The side-effect of standardization of data, is that it becomes easier to share [5].

The consequences of such data structuring can be seen in international financial reporting. The Organization for Economic Cooperation and Development, or OECD, worked with U.S. authorities to come up with an international standard for data sharing relevant to taxation and financial reporting [6].Using this common reporting standard, more than 90 nations now automatically share and exchange information on residents, assets, income, revenue, and other cogent information [6].

What this means for tax reform is that multinational reporting equips the IRS and Congress to better understand the behavior of U.S. corporations abroad, relationships between taxpaying entities in multiple nations and under a range of jurisdictions, and how their money is moved, stored, and invested [6]. This facilitates better estimates of how changes to corporate income tax, as well as related proposals like the Border Adjustment Tax, could affect the global flow of profits and potential revenues. The same predictive analytics potential associated with corporate tax reform can also be leveraged to analyze, predict, and reform individual income taxes.

Share of Federal Tax Revenue

Taken together, lawmakers, the IRS, and national governments have more insight into the taxation patterns and behaviors of their own countries, and the world, than ever before. As tax reform continues to evolve, there is more potential to turn this analysis into foresight, modeling the financial impact of every element considered as part of a comprehensive reform bill [3].

The New Role of Tax Professionals

In the world of accounting and tax law, big data and analytics are closely associated with automation [7]. The more processing power and data management that can be offloaded to computers, the less time humans will have to spend manually crunching numbers, constructing models, and conducting independent analysis [7]. However, this does not spell the end of opportunity for tax professionals. Rather, it represents many new beginnings, fresh opportunities, and a renewed importance of tax experts working alongside these sophisticated machines [8].

Experts in tax, equipped with an LLM in Taxation, are needed to ensure data scientists and programmers ask the right questions of their big data troves, to interpret the feedback derived from algorithms and data queries, and to provide guidance on the development of future policy [8].

Experienced accountants who have earned a Master of Taxation are likewise needed to shepherd the data that is collected, managed, and organized, to ensure that the analytic potential of modern technology is leveraged responsibly and accurately. As big data enables policymakers to be more scientific in attempting tax reform, experts in tax law and accounting will still be needed to make the new, more robust information accessible, meaningful, and useful [8].

Big data, and the analytic capabilities it supports, has already proven transformative for many industries. Thanks to the internet, few of those living in the 21st century are isolated from the effects of big data. Taxation is no exception to the trend. Individual taxpayers and private businesses are already seeking solutions and opportunities to make tax planning and compliance more painless, simple, and automatic. The government, similarly, is finding new ways to collect, organize, and utilize big data in support of enforcing, as well as reforming, taxes in the United States. Human ingenuity, combined with analytic capabilities and modern technology, represent an exciting new future for taxation.










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