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AI incident #962 ·

Tbilisi-Based Call Center Allegedly Uses AI-Driven Scripts to Defraud Over 6,000 Victims of $35 Million

Open on the AI Incident Database 2 news reports Snapshot 7 Sep 2026

What happened

A large-scale AI-assisted financial scam, allegedly operated from Tbilisi, Georgia, used deepfake celebrity endorsements and manipulated victims through fraudulent trading dashboards that simulated high returns. Call center agents, trained with AI-driven persuasion tactics, convinced individuals to invest more money while falsely promising profits. Many reportedly lost their life savings, with the total losses reaching $35M across more than 6,000 victims.

Only the incident metadata is stored here. The underlying news reports are on the AI Incident Database (CC BY-SA 4.0); use the links above to read them.

News reports (1)

Coverage catalogued by the AI Incident Database. Titles link to the original publisher; the text is not reproduced here.

  1. Deepfakes, cash and crypto: how call centre scammers duped 6,000 people
    theguardian.com · The Guardian · AIID #4841

Who was involved

Alleged harmed party
Small Business Owners, Retirees Seeking Investment Opportunities, People With Neurological Disorders, Individuals Deceived By Celebrity Deepfake Impersonations, Financial Professionals, Elderly Investors, Financial Scam Victims

Classification (MIT AI Risk Repository taxonomy)

Causal entity
AI
Intent
Intentional
Timing
Post-deployment
Harm level
—
Sectors
—
Countries
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Risk entries describing this failure mode

Entries from the MIT AI Risk Repository coded to subdomain 4.3.

  • Impersonation/identity theft

    "Impersonation/identity theft - Theft of an individual, group or organisation’s identity by a third-party in order to defraud, mock or otherwise harm them."

    A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)

  • IP/copyright loss

    "IP/copyright loss - Misuse or abuse of an individual or organisation’s intellectual property, including copyright, trademarks, and patents."

    A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)

  • Dehumanisation/objectification

    "Dehumanisation/objectification - Use or misuse of a technology system to depict and/or treat people as not human, less than human, or as objects."

    A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)

  • Defamation/libel/slander

    "Defamation/libel/slander - Use of a technology system to create, facilitate or amplify false perception(s) about an individual, group, or organisation."

    A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)

  • Financial and business

    "Financial and Business - Use or misuse of a technology system in a manner that damages the financial interests of an individual or group, or which causes strategic, operational, legal or financial harm to a business or...

    A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)

  • Cheating/plagiarism

    "Cheating/plagiarism - Use of another person’s or group’s words or ideas without consent and/or acknowledgement."

    A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)

  • Cybersecurity

    "LLMs may exacerbate cybersecurity risks in various ways (Newman, 2024). Firstly, LLMs may significantly amplify the effectiveness of deceptive operations aimed at tricking people into disclosing sensitive information or...

    Foundational Challenges in Assuring Alignment and Safety of Large Language Models (Anwar2024)

  • Domain-Specific Misuses

    "Improvements in LLMs may exert greater pressure to apply LLMs to various domains, such as health and education (Eloundou et al., 2023). Crude efforts to use LLMs in such domains, however, may incur harm and should be di...

    Foundational Challenges in Assuring Alignment and Safety of Large Language Models (Anwar2024)

Incidents in the same risk subdomain

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