India’s parliamentary finance committee proposes leveraging artificial intelligence to expedite income tax refunds and enhance scrutiny of suspicious claims, amid rising backlog and widespread tax evasion concerns.
India’s parliamentary finance committee has called for artificial intelligence-based checks to speed up income tax refunds while flagging suspicious claims for closer scrutiny, as delayed repayments surged over the past three fiscal years. The panel, chaired by Bhartruhari Mahtab, said the system should allow low-risk refunds to move through quickly while unusual cases are held back for review, reducing the burden on taxpayers and the tax department alike.
The committee said refunds pending for more than 90 days climbed from 12.73 lakh in 2023-24 to 14.18 lakh in 2024-25 and reached 26.96 lakh by January 31 in 2025-26. According to the committee’s observations, the growing backlog points to bottlenecks in scrutiny and processing that are slowing cash flow for individuals and companies that depend on timely repayments.
Its wider report also pressed the tax administration to become more targeted and less intrusive, arguing that technology can help separate genuine compliance from fraud. The panel linked that recommendation to the scale of revenue leakage seen in other parts of the system, including Goods and Services Tax evasion and a large stock of tax dues that officials have been unable to recover.
Parliament has already been told that Central GST officers detected about Rs 7.08 lakh crore in evasion over the past five years through 2024-25, including roughly Rs 1.79 lakh crore in input tax credit fraud. In another update, officials said they found more than Rs 1.88 lakh crore in GST evasion during April-December 2024, made 132 arrests and recovered Rs 20,128 crore. ET has also reported that tax authorities are using data analytics and AI tools such as NETRA, BIFA and ADVAIT to identify suspected fake input tax credit claims, suggesting that the refund proposal fits into a broader move toward automated risk detection.
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