GST evasion: Over 200 companies under lens after data mining

GST evasion: Over 200 companies under lens after data mining

The indirect tax department has issued notices to about 200 companies after
data mining revealed that they may be guilty of evading the goods and services tax (GST). The findings showed that these companies may be under-invoicing or selling their goods in cash to customers. The data mining has raised red flags in situations where details in
GSTR3B and
GSTR1 didn’t match.

GSTR1 is an online form in which details such as value of the product, tax rate and amount of tax are mentioned. GSTR3B is an online form that records sales and purchase details. “It is apparent that the GST authorities have started scrutiny of the returns using the data emanating from the system and the information sought from taxpayers on specific aspects, which are required to be responded to with specific details,” said MS Mani, partner, Deloitte India.

“It is imperative to have the returns doubly verified before vigil on the information sought by the authorities in order to prevent litigation.”

The data mining showed that companies were purchasing products at high prices but there was a mismatch with sales. “You have set off your tax liabilities (GST) by way of payment using input tax credit in excess of 95% of the total tax liabilities… In other words, payment of tax was less than 5%,” read the tax notice sent to a company that EThas seen.

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Tax officials said many of the companies recorded large raw material purchases but little sales, suggesting that they may be under-billing or selling products at lower prices officially and accepting money in cash from buyers for the rest, tax officials said. However, experts said there could be another explanation. “The tax department has issued notices but many companies may have used transition credit from the previous tax regime against tax due and to assume that they have not paid tax on actual value addition may not be fair,” said Sachin Menon, national head of indirect tax, KPMG.

“This mismatch could also be due to the suppliers not uploading all the invoices while the company may have taken these credits as they have received tax invoices and paid for them.” As part of the transition to GST last year, companies were allowed to use unused, old tax credits on their books as transitional credits to set off future GST liabilities.

To assume that tax credits for all businesses only pertain to one month may not be accurate, tax experts said.

Legal experts said that such notices could lead to litigation and some companies may be looking to approach the courts. “While there could be some lapses in a few cases, the entire business community cannot be looked at through the fraud lens,” said Abhishek A Rastogi, partner at Khaitan & Co. “Such notices should be issued after thorough examination as there could be several reasons for less payment through cash.

In case we will find that the notices are arbitrary, we may opt to file writs against such action.” Tax officials on the other hand pointed out that many companies indulge in undervaluing sales and overvaluing purchases. “In the earlier tax regime, such transactions would typically go undetected as there was no mechanism to compare inputs from suppliers with sales,” said one of them. “Underinvoicing and over-invoicing is the biggest method by which most companies escape tax and generate black money.” The government had charted out sector-wise ‘risk factors’ or issues companies might exploit to avoid paying GST. According to the tax official cited above, categorisation or risk evaluation for these audits has been created by using big data analytics.

The government has used statistics of the last two financial years to create the audit checklist. Big data analytics is being used by tax departments since 2016. The tool is deployed to find outliers in any sector and the gap with industry based average taxes is used to determine targets for further scrutiny. “The government would have comparables,” said a person with knowledge of the matter. “Say, if 10 consumer goods companies of a particular size pay Rs 50 crore in taxes, it is unlikely that one company of the same revenue size would pay Rs 1crore. Data analytics could easily point out such anomalies and the lens would then be on such companies.”