In the early days of the COVID-19 pandemic, the issue of tax administration was low on the agenda for most. Beyond the obvious public health concerns, most business and government leaders were focused on how best to keep businesses afloat. But for those in federal, state and local governments responsible for delivering services on already-tight budgets, tax administration has always remained a top priority – and that has only intensified with each round of stimulus packages. Now, from the IRS to state and local tax authorities, pressure is mounting for accurate, efficient, timely tax collection capabilities to deliver much needed revenues to ensure uninterrupted services to citizens.
Just as important, these authorities need more accurate, responsive capabilities for understanding and anticipating the impact of a swiftly shifting tax landscape. In the midst of record-high unemployment and a three-month extension to the US tax season, tax authorities are in some ways flying blind. They have limited ability to provide guidance to government leaders, decision makers, and governing bodies regarding how to budget and plan for the coming years.
Third, tax authorities need more powerful capabilities for detecting and deterring fraud. With unprecedented levels of stimulus funding injected into the economy, along with modifications to existing tax rules, massive fraud has become an immediate concern for government, with the potential to significantly erode government revenues at a time when they are most needed.
These three areas are linked in an important way: Data. In each case, developing a greater capacity for gathering, analyzing, and using data is the fastest and most effective way to deliver more effective tax collection, revenue/budget forecasting, and fraud management capabilities. In this post I offer a high-level view of how tax authorities can use data management and advanced analytics capabilities to better accomplish these goals today. These insights build on the work SAS has done with the World Bank Group’s Prosperity Collaborative, a multi-stakeholder initiative for helping countries create better tax systems through innovative technology.
Analytics for complex tax administration challenges
Effective tax collection requires tax authorities integrate data from across government agencies to construct a more comprehensive, 360-degree view of individuals and corporations. For a simple example, consider how the IRS could combine data from state and local tax authorities, social media, corporate registrations, public domain data, the Small Business Administration, and the Social Security Administration to better understand when an individual or corporation experiences a change in status.
At its core, this is a data challenge. How can the IRS routinely gather this type of data from peer agencies (in line with federal regulations regarding information sharing within the government) and make sense of it in order to create a richer, fuller view of millions of individuals and corporations ? Advanced analytics capabilities are the only way to do so at scale.
The value of analytics rises in the face of greater complexity. For example, large global organizations are among the most difficult for tax authorities to understand, sometimes with dozens or hundreds of individual business units with headquarters in multiple countries. In these cases, governments need to find better, more effective ways to work together and share data, which will arrive in different formats and from different systems. Individual analytics tools aren’t enough to handle this type of complexity. Making sense of this type of disparate data, at scale, requires a mature analytics platform.
Forecasting and analysis of stimulus effects
It is difficult to predict the effects of stimulus packages on government budgets. In the US, where multiple packages have been passed and with possibly more on the way, stimulus-related budget uncertainty is significant. To keep government running smoothly, it is incumbent on tax authorities to help legislators understand and anticipate stimulus impact in order to set budgets, adjust tax levels, and pass laws in line with shifting budget realities.
“If we change this tax law now, what will be the downstream impact?” “How do stimulus payments impact uncollected taxes we were expecting before this crisis?” “Who will bear the greatest burden if these new tax laws are passed?”
These are the types of questions legislators ask as they plan for the impact of stimulus packages – and for the answers, they need the critical insights of tax authorities.
Microsimulations that draw from a range of data -- not only proprietary government data, but “open” economic and other data from non-government sources -- can support better decisions. Governments and businesses apply advanced analytics to such tasks every day, replacing what was once an array of manual spreadsheets with algorithm-driven models that can be modified and executed in moments.
Huge stimulus requires fraud detection and deterrence
In the midst of massive economic stimulus, governments know to expect some level of fraud, however unwanted. But what level of fraud is the government willing to accept? After all, the potential implications of widespread fraud grow along with the size of the economic intervention, and 2020 has seen some of the most significant interventions in history. What could historic levels of fraud look like, and what impact could they have on the long- and short-term functioning of the economy?
Fortunately, technological capabilities have advanced significantly since the economic crisis of 2008, which was accompanied by massive stimulus and bailout packages. Tax authorities can deploy analytics technologies and capabilities to combat fraud, identifying signals hidden deep in the data that can reveal (for example) shell companies created solely for the purpose of receiving stimulus payment, or even falsified individual identities created by identity thieves.
These types of advanced capabilities are useful in identifying and pursuing fraud that has already occurred but, more importantly, in preventing fraudulent payments from ever being made. Having both backward- and forward-looking fraud detection and deterrence capabilities are critical in an environment marked by ongoing disruption and uncertainty.
Combine analytics and processing power for optimal tax administration
Recent advances in analytics capabilities enable the powerful capabilities described here, but they cannot function properly in a vacuum. For example, a “whole of government” approach to combating tax fraud and ensuring compliance requires multiple government agencies (and in some cases, multiple governments) working together, connected by their data. New processes and a higher level of organizational coordination must accompany new analytics capabilities. Just as with any other analytics endeavor, technology is only part of the equation. These advances also require massive processing power – not a new challenge for government, but in this case the scale of analytics needs demand rock-solid processing capabilities behind the scenes.
Are these new capabilities worth the effort of implementing them? Absolutely. Just consider the enormous amounts of data pulsing through tax authorities today, much of which is being captured and stored but not analyzed. This data holds enormous potential to support more efficient, effective tax administration – but only for organizations that have the capacity to use it. That’s where SAS analytics capabilities and Intel processing power can make all the difference for tax authorities and the governments that rely on them to operate more effectively and, increasingly, to provide key insights to drive executive and legislative decision making.
For more insights on how governments around the world are addressing tax issues in response to the pandemic, please view the recent World Bank-hosted webinar, Technology and Tax During and Beyond the Coronavirus Pandemic. You can also visit the SAS COVID-19 Resource Hub to learn more about how the public sector can apply SAS technology to the pandemic's challenges.