How Internal Revenue Service Uses Big Data and Data Analytics

Topic 2

You mentioned that all companies should have the same controls over customer data.  What key elements would you make sure all companies have in place and what may the impact be on a smaller company vs a larger company?

 

Topic 1

You mention the cost of tax fraud is in the billions.  How do you think the IRS could specifically use data analytics to detect this type of fraud?

 

 

 

 

 

How Internal Revenue Service Uses Big Data and Data Analytics

According to Houser and Sanders (2017), ‘the IRS is the branch of the United States Department of Treasury that is responsible for administering the Internal Revenue Code and enforcing tax law.’ The Internal Revenue Service uses a range of analytic ways to handle issues like ID thievery, refund fraud, optimization of inventory and other various activities that are within its statutory mandate. Analytics is very vital in the management of risk and decision making in the IRS.  The IRS uses big data analytics to access commercial and public data. This data helps in identifying tax evaders. For example, IRS was able to resolve over $3.7 million tax the ability to easily verify tax return information allowed the IRS to resolve more than 3.7 million tax return inconsistencies in 2015 which totaled more than $ 6.3 billion.

According to Alarie, Niblett, and Yoon (2019) data analytics help the IRS and other tax regulators in administering tax law effectively. The IRS has large amounts of data which it uses to identify the tax gap, that is, the tax it actually collects and the tax it would collect if all taxpayers were compliant. They further argue that ‘predictive analytics can be used by tax authorities to optimally allocate their scarce resources and more precisely target enforcement efforts to yield optimal results, including identifying and pursuing taxpayers who are less likely to comply with their obligations under the status quo.’

According to Olavusrud (2019), the IRS uses ‘data analytics to detect and combat fraud, the Government Accountability Office (GAO) estimates that criminals attempted at least $14 billion in identity theft tax refund fraud in 2015, and the Internal Revenue Service (IRS) paid out at least $2.24 billion on that amount.’ In an attempt to address this issue, the IRS opted for advanced data analytics.

Customer Data Control and Use

Managing customer data is a controversial issue. According to Kerber (2016), ‘consumer data is now the world’s most valuable resource—”the oil of the digital era”—and needs to be treated and safeguarded as such. Failing to do so can result in serious damage.’ For example, in 2016 Uber’s database was hacked and private data of approximately 57 million people were accessed. This affected the reputation of Uber Company and it cost them $148 million in settling the damages. Organizations need to ensure proper collection and management of consumer data to avoid breach as this leads to loss of customer trust.

According to Murphy and Martin (2016), ‘the average cost of a data breach is $3.86 million. It’s estimated that small and medium-sized businesses (SMBs) experience average losses of $120,000 per data breach.’ When an organization gathers personal information about its customers they are obligated to keep the data safe. This data should not be accessible to everyone as it includes personal information like their phone numbers, home addresses, and even their company financials. This is a way for businesses to build trust with their customers. According to Kerber (2016), businesses should consider investing in a customer relationship management tool which is a great way to organize and keep customer data safe. For example, Expensify started using Zendesk Sell to store client information. Sell has kept the company’s data safe and also highlights potential improvement opportunities.

All organizations should have the same controls over customer data. All customer data should be treated with equal measure as no data is superior to the other. According to Roundtree (2016) ‘investing in the security of your data is an investment in your company’s future, which is money well spent.’

References

Alarie, B., Niblett, A., & Yoon, A. (2019). Data Analytics and Tax Law. Available at SSRN 3406784.

Houser, K., & Sanders, D. (2018). The Use of Big Data Analytics by the IRS: What Tax Practitioners Need to Know. Houser, Kimberly and Sanders, Debra, The Use of Big Data Analytics by the IRS: What Tax Practitioners Need to Know (February, 2018). Journal of Taxation128(2).

Kerber, W. (2016). Digital markets, data, and privacy: competition law, consumer law and data protection. Journal of Intellectual Property Law & Practice11(11), 856-866.

Martin, K. D., & Murphy, P. E. (2017). The role of data privacy in marketing. Journal of the Academy of Marketing Science45(2), 135-155.

Olavusrud, T. (2019). IRS Combats Fraud with Advanced Data Analytics. Get over It. SMU Sci. & Tech. L. Rev.19, 3.

Roundtree, B. (2016). U.S. Patent Application No. 15/171,997.