Impersonation Scams Surge 1400% as AI Fuels Crypto Fraud Now

Impersonation crypto scams surged 1,400% in 2025 and average losses rose 600% as AI-driven fraud scales, prompting calls for real‑time detection and cross‑border cooperation.

  • Impersonation scams targeting crypto users surged about 1,400% year‑over‑year in 2025.
  • The average loss per impersonation attack rose by over 600%, indicating larger single losses.
  • Scammers posed as trusted services, including an operation that stole nearly $16 million from victims linked to a case now indicted by prosecutors.
  • Use of Artificial Intelligence made scams roughly 4.5 times more profitable and increased operational scale.
  • Authorities and industry are urged to expand real‑time detection, mule tracking, and cross‑border cooperation, though no single fix exists.

According to Chainalysis, impersonation scams against cryptocurrency users rose roughly 1,400% year‑over‑year in 2025, with the average amount stolen per incident increasing by more than 600%. The report links the rise to fraudsters posing as trusted people or organizations to obtain crypto, passwords, account access and other sensitive information.

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One high‑profile case involved scammers impersonating Coinbase, resulting in nearly $16 million stolen from victims; the theft is the subject of an indictment by the Brooklyn District Attorney’s office, and the accused has pleaded not guilty. The report notes many schemes combine impersonation with other tactics such as pig‑butchering, social engineering and wallet‑focused fraud.

The study highlights growing use of artificial intelligence and specialized vendor tools, which have contributed to what the authors call an “industrialization of fraud.” The analysis found scams using AI were about 4.5 times more profitable and showed higher daily revenue and transaction volume. “These metrics suggest both higher operational efficiency and potentially broader victim reach,” the report says.

Chainalysis recommends increased prevention measures, including wider adoption of real‑time fraud and mule detection systems and more resources for cross‑border law enforcement coordination and support in low‑capacity jurisdictions. The authors caution there are no simple solutions, noting “There are no silver bullets to tackling such entrenched, industrial-scale scamming activity and to be effective, a multi-pronged response is required.”

A graphic accompanying the findings was shared by Chainalysis. Additional context is available in the editorial policy.

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