If an individual today thinks that he can evade Gst tax and get away with it, he is grossly mistaken! There is a huge amount of difference in the way India functioned in the yesteryears and in the present age. The Government today, is aware of the technological revolution and the immense benefits that can be drawn from it.

Big Data takes GST Tax Defaulters to Task

The GSTN Group of Ministers (GoM) in collaboration with Infosys has leveraged data analytics to identify GST tax defaulters. The GSTN Group of Ministers (GoM) has directed the state governments to take GST defaulters to task.

With the help of Infosys-built data analytics, the ministry has been able to identify large number of defaulters for filing of while filing GSTR3B and GSTR1. Two reports have been compiled which will be shared with State governments and strict action will be taken against those who have defaulted. The analysis revealed that 34 per cent of businesses paid INR 34,400 crore less tax between July-December while filing initial summary return (GSTR-3B).

GST tax was launched by the Government of India in July 2017 and the past one year has been a mixed bag for tax filers as well as the Government. The retaliation against GST, the disputed tax rates & slabs, classification of commodities etc. coupled with the technical glitches in implementation were few of the teething troubles for GST.

Things settled slowly over the year and companies/ professionals realized the logic behind the GST tax. However, some companies tried to fulfill their malicious intents. Revenue authorities have discovered various cases of tax evasions wherein companies have submitted doctored bills to claim input tax credits. But not anymore!

Bihar CM and the GST Network Panel head Sushil Modi said that they would use analytics would use a 360 degree approach to track online transactions and identify discrepancies.

So how does Data analytics actually work to prevent tax evasion?

  • Data collection through huge resources

Phone call records, emails, social media accounts, PAN card details, tax return statements, TAN number, property purchase, other high value purchases and travel details, and the list is endless. There is variety of structured as well as unstructured data which is a treasure trove for analysts and data scientists. These data when tracked shows a pattern which can be decoded to raise red flags.

  • Establishing linkages in data segments

After sifting through the humongous data, analysts then resort to grouping and segmenting the data collected from various sources. Taxpayers are grouped on the basis of location, type of return filed, nature of business, age of the organisation etc.  Post segmentation of the voluminous amount of data, analysts then work towards establishing linkages that could help them understand the tax evasion pattern. The pattern is then dissected further to understand the actual defaulters, frequency of their defaults, the tactics used to evade the taxes, and many similar underlying reasons. Tax authorities have come across many unique ways in which taxpayers are evading GST tax. There are cases of evident under-reporting, window-dressing of facts as well as creation of single email id for registrations in four states to escape taxes. Data analytics also aims at finding such tactics used in tax evasion and making a note of this.

  • Mapping the data

Leading technology and data analytics companies are together working on a big platform to use data mapping to identify the locations where tax evasion could be rampant. The geographical coding of the postal address of the businesses will be undertaken. The Tax department will then analyse the areas wherein the wealth and asset creation is higher as compared to the tax collected. However, a lot of data cleansing and standardization goes into this process to derive accurate results.

  • Quantifying and assessing the impact

Demonetisation and GST tax are both aimed at formalizing the economy and eradication of parallel economy. The Government is very clear about its goal to increase its tax to GDP ratio. With a clearly defined result, it becomes easy for analysts to assess and quantify the impact of the default. The indirect tax to GDP ratio for India has risen from 5.2% in 2016-2017 to approximately 5.5% in 2017-18.

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Analytics is the backbone of GST framework

Tax evasion is a crime and it can derail India’s economy single-handedly. The Government is taking every possible step to curb tax evasion such as e-filing, voluntary-declaration scheme, billing software, authorised payment gateways etc.  To achieve this, it has also taken the right step by leveraging Big Data. Ever since its rollout, GST has become a hotspot for data analytics. The GST Network has formed IT as the backbone for the implementation of the tax. Apart from Infosys, many other tech giants such as SAP, Microsoft, Intuit have come out with their own cloud-based GST compliance platforms and accounting software. In a nutshell, GST has brought about immense scope for IT and analytics in years to come. Just like Infosys has held out the net to catch tax defaulters, hopefully other analytics platforms will also facilitate the most optimum implementation of GST.