Crypto Influencer Scott Melker Warns of AI-Powered Scam After Victim Loses $4M

Melker, known for The Wolf of All Streets podcast, took to X on April 23 to share the disturbing news. He revealed that a private investigator had contacted him to disclose that a client had fallen prey to a Nigerian scam group using AI-generated deepfakes of Melker’s identity.

“They sent him a fake driver’s license to prove it was me (with my Twitter avatar as the photo). I will once again reiterate. I will never ask anyone for money, never DM you and I have no fund or investment group of any sort,”

Melker stated. He added that the scammers spoofed accounts of his wife and children to solidify the deception.

The scam appears to be part of a broader trend of increasingly advanced fraud operations. Another trader, known online as “TheChartGuys,” confirmed that similar tactics were used in a separate incident where a victim lost $5,000. The scammers replicated their voices using AI, showing how advanced this technology has advanced in enabling cybercrime.

Crypto trader “Nebraskan Gooner” weighed in on the issue, pointing out that while the fake ID used in Melker’s case had apparent inconsistencies—such as mismatched date and address formats—the operation was otherwise alarmingly advanced.

“Sucks that these scammers are getting so sophisticated but surprised how badly this was with how sophisticated of an operation these seems to be,”

he said.

This wave of AI-driven scams is not isolated. The California Department of Justice identified seven new types of AI-powered crypto-related fraud earlier this year. The California Department of Financial Protection and Innovation (DFPI) also joined forces with the California DOJ to combat cryptocurrency scams, successfully shutting down more than 26 fraudulent websites and uncovering $4.6 million in consumer losses. 

In a recent cybersecurity report , Microsoft echoed these concerns, stating that cybercriminals exploit AI tools to harvest information and create compelling social engineering attacks.

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