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Artificial intelligence has taken impersonation scams to new extremes. Trusted economists and financial experts are being digitally cloned using deepfake technology, luring unsuspecting users into high-stakes frauds. Social media giants, especially Meta, appear slow to reactâraising serious concerns about platform responsibility, financial risk, and public trust.
In recent months, deepfake videos featuring some of the most respected names in global finance have been popping up across Instagram and Facebook. These realistic clips portray economists like Gary Stevenson, Abby Joseph Cohen, and Scott Galloway pitching exclusive investment tips via WhatsApp groups. Some even claim massive recent returns, enticing viewers with promises of rapid, high-profit trades.
But thereâs a catch: none of it is real.
These videos are the product of generative AI and deepfake technologies designed to closely mimic real individuals. Their goal? To scam viewers into transferring money, buying fake courses, or joining fraudulent pump-and-dump schemes. And although many of these fakes are sloppily doneâwith glitchy movements and imperfect voice syncingâtheyâre still fooling thousands.
Even worse, these scams are being actively promoted through Metaâs advertising platform, showing up in paid ads and feeds. Despite being flagged by users, many remain live for days. Metaâs response has been tepid at best, often citing that the content âdoesnât violate community guidelines.â
Financial influencer Micha Catran, who has himself been impersonated, reports having to spend more time defending his identity than creating content. Victims even claim to have sent him money for fake courses he never offered. âI tell them it wasnât me,â he says, âbut the damage is already done.â
Scamming is not newâbut with AI, itâs faster, cheaper, and far more believable. Historically, cons relied on charm or primitive deception. Today, a laptop with access to GenAI tools can replicate faces, voices, and settings within hours. And while platforms like Elon Muskâs X take impersonation seriously and respond within a day, Meta often allows scams to fester even after receiving multiple reports.
The consequences are serious: lost funds, ruined reputations, and diminished trust in both individuals and platforms.
What Undercode Say:
From a cyber-security and digital integrity standpoint, this trend marks a new chapter in online deception. Hereâs our breakdown of the implications and the technical realities fueling this AI-powered scam wave:
1. Deepfake accessibility is now democratized.
Thanks to tools like DALL¡E 2, ElevenLabs, and others, it no longer takes a Hollywood budget to generate convincing video impersonations. Anyone with a bit of tech savvy can craft a scam.
2. Scam monetization is optimized.
Scammers no longer rely solely on phishing emails. With platform ad systems (like Metaâs), they can target demographics likely to trust financial figures. Return on investment for fraudsters can be massive.
3. Metaâs reactive, not proactive.
Automated moderation tools arenât catching obvious fakes. Worse, when flagged, platforms often allow the content to stay live. It points to serious gaps in enforcement and possibly conflicts of interestâad revenue vs. user protection.
4. Victim-blaming dynamics are emerging.
In many cases, victims are blamed for being gullible. But the real issue lies in how convincing these scams are becoming and how platforms give them legitimacy through paid visibility.
5. Trust erosion hits creators hardest.
When a creatorâs likeness is used in a scam, it affects their brand and livelihood. Followers may disconnect out of fear, reducing engagement and credibility, even if the creator is completely innocent.
6. Disinformation spreads faster than correction.
Even when a scam is identified, damage control is slow. Fake content spreads rapidly, while warnings and corrections reach fewer people. Algorithms often favor âengagingâ content, not accurate content.
- Thereâs a need for watermarking and detection tech.
Governments and platforms must push for watermarking standards for AI-generated content. Simultaneously, AI-detection systems must be updated regularly to keep pace with deepfake sophistication.
8. Legal frameworks are lagging.
Few jurisdictions have laws tailored to AI impersonation. The legal grey zone makes prosecution difficult, and scammers often operate internationally, complicating enforcement.
9. Scams now scale like SaaS.
AI has turned fraud into a scalable service. Deepfake-as-a-service tools are emerging, allowing even non-technical users to create convincing scams. The attack surface is expanding exponentially.
10. Educating the public isnât enough.
While user awareness helps, itâs not a full defense. Scams are designed to bypass suspicion, using trust in known figures as the key exploit. Defense must include systemic action from platforms and regulators.
11. Platforms must be held accountable.
Relying on user reporting is insufficient. Proactive moderation, swift takedowns, and better vetting of ad buyers are essential. If Metaâs systems allow paid scams to flourish, the system is broken.
12. Economic consequences could compound.
Beyond individual losses, widespread scams create financial instability. Fake financial tips can distort small markets, and broader mistrust can slow real investment and innovation.
13. A new battleground in information warfare.
The tactics used here may extend into political, health, and societal domains. If AI can impersonate economists today, it can impersonate politicians, doctors, or activists tomorrow.
14. The psychology of authority is weaponized.
People trust figures with credentials. When that trust is simulated by AI, it’s used as a tool of coercion. The scammerâs leverage is your past admiration of a voice or face.
- This will get worse before it gets better.
AI deepfakes are in their infancy. Improvements in realism, speed, and accessibility are constant. Regulation and detection must race to catch up.
Fact Checker Results
- Deepfake videos impersonating economists have been confirmed by independent cybersecurity analysts and major media sources.
- Meta has acknowledged reports but continues to fail in timely removal of fraudulent content.
- The scam methods mentioned (WhatsApp groups, fake courses, pump-and-dump) match known fraud playbooks used globally.
Prediction
AI-generated financial scams will escalate in frequency and realism through 2025 and beyond. As large language models and generative media tools become more advanced and accessible, scammers will begin blending fake video, fake voice, and even deepfake livestreams in real-time. Expect AI-generated financial advice âinfluencersâ that never existed to emerge as personas with thousands of followers. Without decisive regulatory action and platform accountability, the line between real expertise and digital deception will blur dangerouslyâthreatening trust in both social platforms and financial institutions.
References:
Reported By: calcalistechcom_c072de0fd76b9871536351d0
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