On Sunday, the Central Bureau of Investigation (CBI) has pressed a detailed chargesheet against 17 persons of whom 4 are Chinese citizens and 58 companies as accused of their role in a huge transnational cyber crime scam that robbed over 1000 crore to unsuspecting victims.
In October, the agency broke the racket and revealed one and properly-coordinated syndicate using a complex digital and financial ecosystem. The network was entangled in various types of online frauds such as fraudulent loan applications, fake investment schemes, Ponzi and multi-level marketing schemes, fraudulent part-time employment offers, and fraudulent online gambling sites.
The accused, according to the CBI findings, laundered illegal proceeds via 111 shell companies with the help of mule bank accounts to launder close to 1000 crore of money. A single account alone got in excess of 152 crore in a brief timeframe. These shell entities were incorporated purportedly through the use of dummy directors, fabricated or deceitful documents, fictitious addresses and counterfeit statements of business operations.
The inquiry linked the origins of the scam as early as 2020, in the COVID-19 pandemic. The shell companies have been allegedly established on the orders of four Chinese handlers, who are Zou Yi, Huan Liu, Weijian Liu and Guanhua Wang. Their Indian partners supposedly acquired identity records of unsuspecting people, which were further abused to form shell companies and open mule accounts with a view to laundering money and covering up the trail.
CBI also found digital evidence to prove that the operation was under foreign control. Interestingly, in August 2025, a UPI ID associated with an Indian accused was active not only outside the country, but in real time, showing that the fraud network was being supervised abroad in real time.
Investigators reported that the syndicate employed sophisticated technologies like Google ads, mass SMS campaigns, SIM-box-based messages, cloud technology, fintech systems and layered banking systems to avoid detection.
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