Fake Exchange Fraud as a Data Problem, Signals That Analysts and Builders Can Use
How U.S. Federal Records Can Guide Better Fraud Detection and Public Warnings By Md. Tauhid Hossain Rubel Doctoral Candidate and Researcher | Artificial Intelligence, Data Analytics, Cybersecurity & Financial Intelligence, United States This is a short companion to my full article on Medium…
Fake cryptocurrency exchanges in the United States have defrauded Americans of billions of dollars, with losses exceeding $11 billion reported by the FBI in 2025. These scams rely on deceptive information, such as false licenses, expert endorsements, and misleading deadlines, making them a data problem that can be addressed through data analytics and public warnings.
The SEC has documented a case in December 2025, where four investment clubs were misled by fake exchanges Morocoin, Berge, and Cirkor, which falsely claimed to hold government licenses and held fraudulent token offerings. The CFTC has also identified similar scams, where websites merely mimicked legitimate trading platforms, and the money taken from investors was sent overseas.
The SEC and CFTC can act on fraud after schemes are discovered, but early detection and prevention through data analysis can significantly reduce losses.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
