A new SAS Analytics-sponsored, Conference Board of Canada report on how to more effectively collect from tax cheats suggests using more data analytics software as a solution.

Released on Feb. 13, the report doesn’t attempt to calculate Canada’s “tax gap” – the money that the Canada Revenue Agency (CRA) should collect from tax evaders, but doesn’t – it assumes Canada’s situation is similar to that of other countries that have done quantitative reports. After looking at the gap in other countries such as the U.S. and the U.K., the report estimates Canada’s own tax group between $8.9 billion and $47.8 billion.

Not much information is available about how much revenue Canada could be using to tax havens or “shadow market” activity, the report states. In June 2016, a CRA conceptual paper estimated an average annual loss of 5.6 per cent annually on HST and GST collection between 2000-2014.

Another study by the World Bank estimates that Canada’s “shadow economy” – legal activities shielded from public authorities – was 15.3 per cent of its GDP in 2007. Using that estimate, a 2011 study by the Tax Justice Network estimated tax revenue loss of $81.2 billion across all levels of government. But in 2013, Statistics Canada estimated a much smaller shadow economy of just $45.6 billion.”

“This finding highlights the inherent difficulties and wide variation associated with measuring the tax gap,” the Conference Board report states. “Complicating efforts to assess the tax gap is the lack of a standard method for estimating the size of the shadow economy.”

Canada's tax gap - estimates
A table in the Conference Board of Canada report on the tax gap contains estimates made by different studies.

However large the “tax gap,” we can be sure that the CRA would like to recoup as much of that lost revenue as possible. To do so, it might consider increasing its use of data analytics software, the report recommends. In addition to other measures like simplifying the tax code and spending more on enforcement measures, data analytics could help identify cases where there’s a high risk of tax evasion.

“In the U.K., HMRC has invested in advanced analytics to derive risk profiles and improve the targeting of resources to boost tax compliance,” the report states. “A 2014 report showed that these investments enabled HRMC to increase its year-to-date yield by £2.6 billion while employing 40 per cent fewer staff.”

The Office of the Revenue Commissioners in Ireland also used predictive analytics to identify cases for audits, the report states. It found a pilot project to identify high-yield cases to be successful. The U.S. IRS bears another example of an agency looking at software to help it identify many different potential tax problems.

 



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