Mainland Chinese HKUST student loses HK$1 million to scammer posing as official
A mainland Chinese university student in Hong Kong has lost about HK$1 million (US$127,500) in a scam, in which the fraudster claimed she was involved in a criminal case and demanded “guarantees” from her. Police said they received a report from a 19-year-old mainland woman who was student at the Hong Kong University of Science and Technology (HKUST) at 1.07am on Sunday. The force said the victim…
A mainland Chinese student studying at the Hong Kong University of Science and Technology has reportedly lost HK$1 million (approximately US$127,500) due to a scam. The incident came to light when the 19-year-old victim reported a series of calls from an individual impersonating a government officer. This fraudulent caller informed the student that she was implicated in a criminal case on the mainland and demanded HK$1 million in "guarantees" to resolve the situation.
The student complied with the request, but soon realized she may have been deceived and reported the matter to the police. The case is being treated as deception. According to the police, this is not an isolated incident; in the first three months of the year, 42 mainland students in Hong Kong have fallen victim to scams involving fraudsters posing as mainland authorities.
The losses incurred by mainland students in telephone deception are found to be 2.9 times higher than those of local students, with an average loss of HK$690,000 for mainland students compared to HK$176,000 for local students. In response to this growing issue, the police have intensified their anti-scam efforts specifically targeted at mainland students.
These efforts include social media campaigns, seminars, and surveys. Major universities in Hong Kong, including HKUST, have also taken proactive measures by mandating anti-scam training for new students to raise awareness and protect them from such fraudulent activities.
Written by urgent.news from South China Morning Post - Hong Kong's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.