17 April 2018

Big Data

The Use of Big Data Analytics by the IRS: What Tax Practitioners Need to Know' by Kimberly Houser and Debra Sanders in (2018) 128(2) Journal of Taxation comments
With the budget reductions and losses in staff over the past several years, the IRS has been forced to do more with less. In turn, the IRS has turned to big data analytics make up for its loss of personal and the impact of the budget reductions. In 2011, the IRS created the Office of Compliance Analytics in order to create analytics programs that could identify potential refund fraud, detect taxpayer identity theft, and become more efficient in handling noncompliance issues. The IRS uses a wide range of analytic methods to mine public and commercial data including social media sites such as Twitter, Facebook, and Instagram. The data collected from this mining is combined with IRS’s own proprietary information and analyzed using pattern recognition algorithms, which help to identify potential noncompliant taxpayers. The current ability to continuous monitor financial and personal behavior facilitates the building of exhaustive histories of individuals. Knowing that the IRS is utilizing public internet data from websites such as Facebook, taxpayers should consider that their posts could impact their probability of audit.
‘Data Science, Data Crime and the Law’ by Maria Grazia Porcedda and David S. Wall in Research Handbook on Data Science and Law (Edward Elgar, 2018) comments
This chapter explores the relationship between data science, data crimes and the law. It illustrates how big data is responsible for big data crimes, but that data science and law could mutually help each other by identifying the ethical and legal devices necessary to enable big data analytic techniques to identify the key stages at which data crimes take place and also prevent them. The first part looks at the strengths and weaknesses of data science (big data analytics). The second part explores the data crimes created by Big Data to understand their risks, threats, and harms. The third part discusses the opportunities and limitations of the use of data science in surveillance and criminal prosecution to consider whether the predictive (anticipatory) qualities of Big Data analytics could be applied to identify Big Data Crime.