There are more than 100 software geeks working in IT Systems Directorate of Income Tax Department located in New Delhi. They use analytics tools to mine, crunch and spot insights and patterns to detect tax fraud. Since taxpayer data is confidential, tax officials are trained internally to manage IT systems to oversee this work. Some parts of the job are outsourced to data analytics firms to extract deeper insights.
The department works with mixed data from multiple sources to which it has access; These include direct and indirect taxes, motor vehicle registration, passport, import and export data, SEBI data, stock exchange data, investment data including foreign investments, data collected from tax administrations of other countries and such as data from country-by-country reporting of the OECD. Are included. (CBCR). A mine of taxpayer data is stored in the Tax Information Network, TIN, for processing; The quantity is increasing manifold.
The stated goal of the department is to widen the tax base which will lead to an increase in tax collection in relation to GDP. Today, in India, less than 4% of people file tax returns, and a few lakh admit that their income is . More than 10 lakh per annum, which explains the low level of tax collection in relation to GDP. Perhaps, this explains why Finance Minister Nirmala Sitharaman, in her budget speech in Parliament in February this year, invoked a verse from the Mahabharata, saying that tax collections are indeed commensurate with Religion,
The good news is that the advance tax collection has been really fast this year amid early recovery. Advance tax collection touched 33 per cent during April 1 to June 16 1,01,017 crore as against 75,783 crore in the year-ago period, swelling the direct tax revenue of the government. The base effect of hit to collections during the COVID pandemic and the second wave of lockdowns, and higher profitability and sales performance of large companies on the back of increased consumer demand after the economy reopens. -Down, those are all factors which have led to an improvement in tax collection. Equally important is the tax department’s growing reliance on data analytics to mine information and track the source of unaccounted wealth.
There is a growing realization that the use of IT platforms and analytics and new generation technology tools such as artificial intelligence and machine learning is the best way to increase collections. Tax administration in advanced economies has recognized this potential and has taken great advantage of the use of analytics tools in digital tax administration. Real-time or real-time data analytics engines used to validate invoices and lag discrepancies, cross-check sales against purchase declarations, verify pay and intercept declarations, and compare data across jurisdictions and taxpayers goes.
Big data refers to the amount, velocity, and variety of data that is growing manifold from different sources and the speed at which it can be processed. Analytics is the way to extract value from this data. Tax data analytics combines technical knowledge of tax laws, large sets of data that are analyzed computationally to reveal patterns and trends in tax fraud, and techniques such as machine learning, AI and visualization to generate insights. uses it.
The Income Tax Department in India has a mine of information. Individuals and corporations file tax returns. Data also flows from third party sources including banks, credit card companies, property registrars and jewelery houses, through filling in for tax deduction at source, etc. The spending patterns of individuals making large spends in high value transactions are analyzed to identify potential. Taxpayer. Real estate is a means of tax evasion. The information provided is tapped by the registrar of property on those who buy and sell properties above a certain limit. Each financial transaction that is tagged to the tax department’s unique identifier, Permanent Account Number, PAN, enables intelligent analysis of data to get information about the taxation evasion potential of income. The data is also sourced from other government entities such as Goods and Services Tax Network, GSTN, Ministry of Corporate Affairs and SEBI.
When the requirement of annual information return filed by third parties was first introduced, the scrutiny was not as rigorous as PAN was found missing in many large transactions collected through TIN. Getting PAN has become very easy now. A massive full-proof PAN coupled with massive use of data analytics has enabled the tax department to identify and collect tax from people with large incomes, even if they do not file tax returns. This, along with efficient TIN, reduces the reliance on voluntary information provided through the tax return. With the GST roll-out and data sharing between the GSTN and the Income Tax Department, the revenue collection potential has become immense. The tax department should put the quantum of taxes collected using data analytics in the public domain to get a clear picture of its effectiveness.
In fact, the only limit to how much information is collected is the tax department’s ability to meaningfully use the dataset to extract intelligence and insights while protecting the data against misuse.
The scope of data mining and analytics has expanded significantly after the implementation of the Good and Services Tax (GST). GST leaves a digital footprint (read audit trails) in the income and production chain as manufacturers get credit for the taxes paid on the inputs used by them to make the product. Compliance has improved, making it easy to track how much GST a company has paid for the value added by it. Deploying data analytics, the information is correlated with what the company claims as its expenses. It enables tax authorities to check whether a company has declared its income correctly and assess its tax liability more accurately. Consistently following audit trails and data crunching will help in widening the direct tax base. As more and more data is mined, algorithms through machine learning become better and better, making it easier and easier to tap into a unified base of indirect and direct tax efficiencies, and closing holes in tax evasion. Is.
Take an example. A garment manufacturer who claims a lot of credit against taxes paid on inputs without paying cash or adding much value to a product, comes under the scanner of tax collectors. But they need conclusive evidence to establish fraud. Data analytics has been deployed to track all the purchases made by such apparel manufacturer and analyze fish for fake invoices and claims. The entire supply chain is covered – from suppliers of embellishments, fabrics, yarns and, in turn, their suppliers as well. If the yarn supplier comes across different networks of fake invoices, he can be caught, his bank account details can be checked. Thus, the investigation extends to other people in the supply chain if they are found to be committing fraud.
Social media data is one of the sources used in Big Data. In the UK, the Department of Revenue and Customs has developed a computerized data mining system of social network analysis software that cross-checks the tax records of companies and individuals with other databases to establish fraud. Software connects analytical tools and collects information and applies predictive analysis. The US Internal Revenue Service also collects social media data and deploys big data to investigate tax evasion. South Korea has developed a big data analytics system based on AI to analyze tax invoices. Indian tax authorities are also building effective systems, tools and feeds to tap this source of data and are also leveraging social media and different types of data to gather intelligence. Using mobile data, textual analytics, geocoding data, audio and video analytics, they are able to move forward in line with what technological tools are able to provide. Many of these are in progress; Nevertheless, given the strength of the Indian IT industry and start-up ecosystem, the tax administration will, at the earliest, take advantage of emerging technology tools to generate revenue. This will give the government more financial space to meet its growing expenditure requirements to aid recovery.