New Delhi, June 30 (SocialNews.XYZ) The Finance Ministry on Tuesday said advanced technologies such as artificial intelligence (AI), machine learning and data analytics are playing a key role in strengthening Goods and Services Tax (GST) administration by helping authorities detect potential tax evasion while reducing compliance burdens for honest taxpayers.
According to the ministry, these technologies are being deployed across critical processes, including GST registration and scrutiny, to analyse data patterns and identify risk indicators. This enables tax authorities to focus enforcement efforts on high-risk taxpayers while easing regulatory requirements for compliant businesses.
The ministry said the adoption of technology has been a significant part of GST's evolution over the past nine years, with the indirect tax system steadily transitioning towards a data-driven and technology-led administration.
Reflecting the expanding formal economy, the number of GST-registered taxpayers has increased from 66.5 lakh in 2017, when the tax regime was introduced, to 1.65 crore as of May 2026.
GST collections have also witnessed steady growth over the years. Gross GST revenue, which stood at around Rs 7.4 lakh crore in 2017-18, has risen consistently. Collections increased from Rs 13.76 lakh crore in 2021-22 to Rs 22.27 lakh crore in 2025-26. During the first two months of the current financial year, April and May, GST collections reached Rs 4.37 lakh crore.
Introduced on July 1, 2017, GST replaced a complex indirect tax structure comprising 17 central and state taxes and 13 cesses with a unified tax regime. The reform, implemented after extensive consultations between the Centre and states, was aimed at creating a common national market under the "one nation, one tax" framework.
The Finance Ministry said the increasing use of advanced technologies has improved the efficiency of tax administration while contributing to broader macroeconomic stability. It noted that technology-driven monitoring has made tax collections more predictable, supporting stronger revenue buoyancy and enhancing fiscal transparency.
Source: IANS
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