10th-Grade Dropout Woman Masterminds Rs.2.75 Crore GST Fraud Using 16 Shell Firms; Four Arrested:

10th-Grade Dropout Woman Masterminds Rs.2.75 Crore GST Fraud Using 16 Shell Firms; Four Arrested Police have recently exposed a massive GST fraud racket of Rs. 2.75 crore orchestrated by a 10th-grade dropout woman. To carry out the fraud, a web of shell firms was created using forged documents to syphon off input tax credit, leading to a loss amounting to Rs. 2.75 crore to the exchequer. Investigation revealed that four accused, including the 10th dropout woman, were involved in running the operations. They all got arrested in a joint raid by the cyber police, crime cell, and local police. The main person behind everything is still on the run.

Also Read

ITAT Condones Delay and Grants Fresh Opportunity After Notices Sent to Outdated Email IDs

The arrested individuals have been recognised as Tabassum, Prashant Benzwal, Daulat Ram, and Sumit Saurabh. As per the police, Tabassum was the mastermind of the entire racket; she played a key role in arranging forged documents, getting bogus companies registered, and coordinating transactions among the members. As per the investigators, the gang of accused were found to be running about 16 shell firms across several districts and using them to generate bogus invoices to claim fake ITCs (Input Tax Credits). The group is suspected of taking advantage of weaknesses in the registration and physical verification system. There are doubts that some insiders may have helped them. Officials who approved verification reports without properly checking them are also being investigated.

Also Read

ITAT Quashes Penalty for Breaching Principles of Natural Justice; Remands Case for Fresh Adjudication

The probe uncovered that the accused showed false purchases and sales on paper without actual movement of goods. This documentary evidence helped them in claiming a fake ITC. Previously, an individual had registered a complaint flagging GST evasion of about Rs. 52 lakh; this ultimately resulted in the exposure of a larger network.