Sars uses data-driven risk detection to uncover noncompliance
The South African Revenue Service (Sars) has collected R115-billion through compliance activities, to date, this year.
About 33% of the R115-billion was collected as a result of the automatic risk profiling of taxpayers using data and machine learning.
Sars is working hard to increase and expand the use of data to improve the integrity of outcomes and enhance its capability to detect instances of noncompliance and says it is encouraged by the early success.
"We still have a long way to go, but are encouraged that our strategic approach is beginning to show early impact,” says Sars Commissioner Edward Kieswetter.
“Sars proceeds from the premise that most taxpayers are honest and want to fulfil their legal obligations. For these taxpayers, we will work hard to assist them by providing clarity and easing the burden of compliance.
“Those, however, who continue to defraud the tax system, must know that they do so at their own peril, as we make progress on the rebuilding and modernisation of the institution. As we observe Anti-Corruption Day, Sars remains committed to build on its capability to enforce the law and pursue those who wilfully or criminally seek to break it,” he adds.
Data from various domestic and international sources are used as input into machine learning models, risk profiling and case selection, which has resulted in positive outcomes in several instances, some of which were previously reported.
Of the previously reported 26 000 individuals with economic activity and/or assets exceeding R1-million, almost 1 000 have been identified as being involved in money laundering and other serious crimes, reports Sars.
"On the government procurement of personal protective equipment, our analysis of the Central Supplier Database (CSD) has highlighted large numbers of vendors who supply services to government who are not tax compliant.
"We identified about 1 900 entities, each earning more than R1-million between March 2020 to May 2021 from contracts with government, totalling R6.3-billion, yet are not registered as value-added tax (VAT) vendors," Sars points out.
A further 2 380 VAT-registered vendors have filed nil returns, despite having earned, collectively, more than R9-billion from government contracts.
"The work of regularising the affairs of these taxpayers is well under way. An initial R220-million in additional assessments have been raised, which resulted in almost R75-million of fraudulent refunds being prevented, with debt collection efforts in progress. In each instance, Sars also considers whether criminal prosecutions are applicable," the agency says.
Additionally, using whistle-blower reports and third-party data sources, and after extensive investigative work, Sars deployed more than 90 employees to execute one of the largest search and seizure operations. This operation has identified 11 entities at four different sites, and 27 taxpayers for potential fraudulent disclosures in respect of a number of years. All assets have been preserved while the investigations proceed.
Of the initial batch of 275 individuals with assets abroad detected using the Automatic Exchange of Information programme, the first 50 individuals have been selected for further scrutiny in relation to assets in tax havens, with more to follow.
"We are working with the US Internal Revenue Service using the Foreign Account Tax Compliance Act, which has assisted in connecting a number of South Africans with links to the Pandora disclosures, and investments into a number of States," Sars says.
International data sources used by Sars include the automatic exchange of information on South Africans with offshore financial assets from about 100 foreign jurisdictions, as well as several mutual administrative agreements with sister organisations.
Domestic third-party sources include banks, retirement funds, medical insurance providers, the properties deeds office, the companies register, the national register of motor vehicles, the National Treasury CSD and the national population register.
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