Purportedly AI-Generated Article Falsely Depicted Australian Broadcasting Corporation Finance Presenter Alan Kohler Interviewing Reserve Bank Governor Michele Bullock to Promote Boulder Sparhaven

An online article reportedly used AI-generated imagery to falsely depict Australian Broadcasting Corporation (ABC) finance presenter Alan Kohler interviewing Reserve Bank of Australia governor Michele Bullock about Boulder Sparhaven, a cryptocurrency trading platform not licensed in Australia. The Australian Securities and Investments Commission (ASIC) cited it as an example of increasingly sophisticated AI-assisted investment scams targeting Australians.

Risk classification

• Primary risk domain: 4 Malicious actors
• Primary risk subdomain: 4.3 Fraud, scams, and targeted manipulation

The incident involves scammers using AI-generated deepfakes of public figures to deceive individuals into investing in fraudulent schemes for financial gain.

Additional risk subdomains

• 3.1 False or misleading information: The scams rely on fabricated news articles, fake interviews, and false endorsements to mislead victims.
• 6.3 Economic and cultural devaluation of human effort: The unauthorized reproduction of public figures' likenesses and voices devalues their professional reputation and trust.

Causal factors

• Entity: Human
• Intent: Intentional
• Timing: Post-deployment

The incident is caused by human scammers intentionally using deployed generative AI tools to create deceptive deepfakes and defraud victims.

EU AI Act risk tier

• Risk tier: 3 Limited Risk

Limited Risk: The report describes the creation of AI-generated content such as deepfakes, which falls under Risk Level 3 due to transparency obligations to ensure users are aware they are interacting with AI-generated content.

AI system and alleged parties

• AI system: generative AI
• AI purpose: Deepfake Video Generation; Deepfake Image Generation
• Behaviour type: Tool
• Alleged developer: Synthetic media generation technology developers, Synthetic image generation technology developers, Deepfake technology developers
• Alleged deployer: Synthetic media creators, Scammers in Australia, Scammers, Investment scam operators, Deepfake creators in Australia, Deepfake creators, Cybercriminals
• Alleged harmed parties: Victims of impersonation scams, Michele Bullock, Investors, General public of Australia, General public, Epistemic integrity, Australian investors, Alan Kohler

Harm severity

Severity ratings are based on this scale.

Highest direct severity in any category: Substantial. Severity is scored from Negligible to Catastrophic in each harm category, for harm the reports describe as caused directly or indirectly by the AI system.

• Physical: direct Negligible, indirect Negligible
• Infrastructure: direct Negligible, indirect Negligible
• Property: direct Negligible, indirect Negligible
• Financial: direct Substantial, indirect Negligible
• Environmental: direct Negligible, indirect Negligible
• Malicious content: direct Minor, indirect Negligible
• Differential treatment: direct Negligible, indirect Negligible
• Civil rights: direct Negligible, indirect Negligible
• Democracy: direct Negligible, indirect Negligible
• Privacy: direct Negligible, indirect Negligible
• Psychological: direct Negligible, indirect Minor
• Epistemic: direct Minor, indirect Substantial
• Child sexual exploitation and abuse: direct Negligible, indirect Negligible

Financial

Reported: Yes, the report describes significant financial losses from these scams.

Directly caused: Scamwatch reported $7.4 million in losses specifically from the top impersonated public figures in FY26. Across all investment scams in Australia, losses reached $837.7 million in 2025.

Inferred additional harm: Given that many scams go unreported, actual financial losses from AI-enabled deepfake investment scams are likely significantly higher than the reported $7.4 million, potentially tens of millions of dollars.

Malicious content

Reported: Yes, the report describes the creation and spread of malicious deepfake videos and fake news articles.

Directly caused: Scammers created deepfake videos and fake news articles impersonating prominent figures like Alan Kohler and Michele Bullock to promote scam platforms.

Inferred additional harm: Thousands of malicious deepfake ads and fake articles were distributed across social media platforms, exposing millions of users to deceptive content.

Psychological

Reported: Yes, the report describes distress and anxiety experienced by the impersonated individuals.

Indirectly caused: Prominent figures like Alan Oster felt 'terrible' and 'appalled' that their likenesses were being used to defraud people, receiving up to 10 messages a week from concerned citizens.

Inferred additional harm: Victims who lost their savings to these scams likely experienced severe psychological distress, anxiety, and trauma, affecting hundreds of individuals.

Epistemic

Reported: Yes, the report describes the creation of fake news articles and fabricated interviews to deceive the public.

Directly caused: Scammers fabricated a heated interview between Reserve Bank Governor Michele Bullock and ABC presenter Alan Kohler about a non-existent algorithm called Boulder Sparhaven.

Indirectly caused: The proliferation of highly convincing deepfakes makes it harder for the public to verify what is real, undermining trust in legitimate media and public figures.

Inferred additional harm: The continuous flood of deepfakes may lead to a broader erosion of shared reality, where citizens struggle to distinguish authentic journalism from AI-generated fabrications.

People affected

• Occurrences reported: 19400
• People reportedly harmed: 740
• People reportedly exposed: 19400

Potential causes

Management

• Prioritizing Ad Revenue: Meta and other platforms monetize and approve scam ads without strict vetting.
• Slow Platform Response: Platforms take down reported accounts slowly, allowing scams to reappear.
• Inadequate Proactive Prevention: Platforms fail to implement robust systems to prevent scam ads beforehand.

Technology

• Generative AI Deepfakes: AI makes fake videos and images highly convincing and hard to detect.
• Automated Scam Ad Approval: Ad systems on social media platforms approve fraudulent deepfake ads.
• Offshore Platform Operations: Platforms operate offshore, making technical enforcement and takedowns harder.

Data Inputs

• Stolen Public Likenesses: Scammers use images and voices of trusted public figures without consent.
• Fabricated News and Reviews: AI-generated fake articles and testimonials create a web of deception.
• Misused AFSL Details: Scammers copy legitimate license numbers to bypass basic verification.

Human Factors

• Trust in Public Figures: Victims trust familiar celebrities and politicians endorsing the schemes.
• Difficulty Verifying Legitimacy: Polished AI content makes it nearly impossible for users to spot fakes.
• Overreliance on Search Engines: Users rely on simple online searches which lead to spoofed websites.

Process and Methods

• Whack-a-Mole Takedowns: Scammers quickly recreate removed accounts and websites under new names.
• Inadequate Ad Screening: Social media ad screening processes fail to block deepfake scam ads.
• Ineffective AFSL Checks: Standard license checks are bypassed by sophisticated spoofing methods.

Regulatory Environment

• Lack of Platform Penalties: Absence of heavy fines for platforms hosting fraudulent AI-generated ads.
• Incomplete Regulatory Framework: Scams protection framework is still being developed to hold platforms liable.
• Offshore Jurisdictional Limits: Regulators struggle to enforce laws on platforms operating from offshore.

Information quality

• Classification confidence: High
• Reason for confidence: The reports from ASIC and major news outlets provide clear, consistent, and detailed accounts of the deepfake investment scams, including specific figures impersonated, financial loss statistics, and regulatory actions. There is little ambiguity regarding the nature of the AI's involvement or the harm caused.

The incident involves the widespread deployment of AI-generated deepfakes of Australian leaders and public figures to conduct highly effective financial scams. While causing millions of dollars in losses and minor epistemic harm to public trust, the incident remains a regulatory and law enforcement issue rather than a direct threat to national security.

• Overall national security impact: Minor
• Scope: Single nation
• Primary target: Australia
• Alleged perpetrator: Unknown

Threat characteristics

• Imminence: Long-term. Represents an ongoing strategic concern and evolving cybercrime trend rather than an immediate national security crisis.
• Autonomy: Human-controlled. AI tools were used strictly as content generation mechanisms by human actors who directed the scams and executed the operations.
• Novelty: Evolved capability. Deepfake technology and financial scams are established, but the high fidelity and scale of generative AI content represent an evolved capability.

Impact by dimension

• Physical security: Negligible. No physical systems, critical infrastructure, or human safety were impacted or threatened by this financial scam incident.
• Information security: Minor. While involving highly sophisticated deepfakes of the Prime Minister and central bank governor, the activity was aimed at financial fraud rather than state-sponsored geopolitical influence operations.
• Sovereignty: Minor. Impersonation of the Prime Minister and Reserve Bank Governor poses minor risks to the perceived integrity of official communications, but did not disrupt core government operations or electoral processes.
• Economic security: Minor. The scams caused significant individual financial losses totaling millions of dollars, but they do not pose a systemic threat to Australia's national economic stability or strategic industries.
• Societal stability: Minor. The proliferation of deepfakes erodes public trust in media and public figures, representing minor societal impact, but did not result in civil unrest or systematic rights violations.