Big Data Analytics to Be Used for Tracking Expenditure Patterns by Income Tax Department

by April 1, 2019

Income Tax department in India is going to use Big Data Analytics tools from April 1, 2019, to keep on the tax evasion. The programme is named Project Insight which is worth Rs 1,000 crore. The tool would trace social media profiles of citizen and tabulate their expenditure patterns observing the visual media including photos and videos uploaded on it.

Subsequently, the Income Tax authorities would be informed if there is any mismatch between the income and expenditure ratio of a person and actions would be taken accordingly.

Reportedly, tax officials were given permission by the Income Tax Department to access the software from March 15, 2019

For example – If someone is spending vacations in a foreign country or buying luxury cars and posting pictures about it on social networking site, then the big data software used by IT department can track it and do the needful action required.

Also, the software would make it easy for the officials to maintain a record of details of any individual or corporate businesses.

The goal is to identify and apprehend the tax evaders and encourage more and number of people to file returns and pay taxes.


Some Peculiar Feature of Insight Project Tool

•  It will incorporate an Integrated Information Management System that would help to take the right decision at the right hour by applying machine learning.

•  The software can also be utilized in gathering information by collecting web pages and documents that can be later examined by Income Tax Department.

•  After Belgium, Canada, and Australia, India will be joining the club of countries which use Big Data Analytics to track tax evasion.

•  The system was first acknowledged in 2010 in Britain and since then it has impeded the loss of approx. 4 billion pounds (in INR Rs 36,942 crore) in revenue.

•  The chances of tax theft are expected to go down as the software would fortify the overall research of filed returns and the selection based on a number of small criteria.