A sophisticated cross-border operation exploiting crypto for illicit gains has been dismantled, culminating in prison sentences for nine individuals in China. The group successfully laundered millions stolen from Indian citizens using Tether’s USDT stablecoin.
According to court findings, the syndicate defrauded over 66,000 victims in India, amassing approximately 517 million Indian rupees (equivalent to about $6.2 million). The operation, masterminded by an individual identified as He, began in May 2023 from rented offices in Shandong Province.
He assembled a team, established overseas servers, and managed communications with Indian entities. The core of the scam involved fake investment platforms, notably one called ‘SENEE’, which promised enticing monthly returns of 8-15% on initial investments as low as 1,000 rupees (roughly $12), attracting victims through messaging apps.
Once deposits significantly exceeded the payouts, the fraudsters would abruptly shut down the platform or convert investors’ holdings into inaccessible equity, effectively freezing the funds. This highlights a common tactic in online investment schemes where initial small returns build false confidence.
The stolen funds underwent a laundering process involving conversion into the popular stablecoin Tether (USDT) via unnamed third-party platforms. This use of crypto tokens like USDT is often favored in illicit finance for its perceived anonymity and ease of cross-border transfer. The USDT was subsequently converted into Chinese yuan or U.S. dollars, with the scammers taking a 15% commission.
To enhance their facade of legitimacy, the group employed elaborate tactics, including using false personas – one member posed as a wealthy Indian woman successful in fund investments. They also fabricated documents and websites, leading the Chinese court to label it a “sophisticated criminal syndicate.”
The court handed down significant penalties, with sentences ranging from five years to nearly fifteen years in prison, accompanied by fines, underscoring the severity of the large-scale financial fraud.