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AII-1000-40497
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Meta's Ad Systems Enable Billions in Scam Revenue While Deleting Regulator Searches

Meta's advertising systems enabled scam ads generating approximately $7 billion in annual revenue, while company staffers reportedly deleted fraudulent ads from regulatory searches to manage the appearance of compliance in Japan. Seven Japanese cabinet agencies jointly demanded the platform implement advertiser verification within six weeks, a measure Meta acknowledged it could execute but declined due to revenue concerns.

7 August 2026 Β· Meta Platforms Β· Fraud, scams and manipulation
7 August 2026
AII-1000-404973 sources ↓
Quick facts
Category
Fraud, scams and manipulation
Country
Japan
Source
NDTV
Date
7 August 2026
Drafted by
Raidu Incident Detection

What happened

Japan's seven cabinet-level agencies jointly demanded on Thursday that Meta and other platforms verify advertiser identities, remove deepfake investment scam ads promptly, and improve advertising transparency. Reuters documents revealed that Meta staffers used a tactic to manage what they called the 'prevalence perception' of scam content by identifying keywords and celebrity names that Japanese regulators were searching in Meta's Ad Library, then deleting the ads that appeared.

Meta confirmed it could implement advertiser verification globally in under six weeks, tested it successfully in at least one market, but chose not to do so because the revenue cost was too high. Internal 2024 estimates cited by Reuters showed Meta earned approximately $7 billion annually from ads related to scam schemes.

Reported impact

In 2025, social media investment fraud in Japan reached 9,538 cases with victims losing Β₯127.4 billion (approximately $804 million), a 46.3% increase over the previous year. The average loss per social media investment fraud case was Β₯12.1 million (approximately $76,400), with most victims aged between their forties and sixties.

Combined losses from special fraud, romance scams, and social-media investment scams hit a record Β₯324.11 billion (approximately $2.05 billion), up 62.8% from the previous year.

Governance controls

Reuters investigation revealed an internal cache of Meta documents spanning four years showing systematic tactics to manage regulator perception of scam content. The approach involved Meta staff identifying keywords used by Japanese regulators to search the Ad Library, then deleting the ads that appeared in those searches.

Classification

Category
Fraud, scams and manipulation
Industry
Media, social platforms and marketing
Country
Japan
Harm status
Actual harm
Intent
Intentional
Timing
Post-deployment
Harm types
Economic or property, Public interest
Harmed
Consumers, General public
Failure modes
Content moderation failure, Manipulation or dark pattern
Consequences
Regulatory investigation, Content removal
AI systems
Meta Ad Systems, Meta Ad Library
Regulations
FCRA (cited)

Sources and evidence

This record was researched and written by the Index, based on the following original reporting.

NDTV
A 2025 Report Showed Meta Earned $7 Billion A Year From Scam Ads
5 August 2026
Gadgets Now
Meta Earned Around $7 Billion Annually From Scam Ads, Internal 2024 Estimates Show
6 August 2026
Tech Times
Meta Had Six Weeks to Stop Japan's $804M Scam Crisis: It Chose Revenue
7 August 2026
Sep 2026Deepfake Scam Uses Bitdefender CEO's Likeness for Fraudulent Investment Scheme in RomaniaConfirmedFraud, scams and manipulationBusiness24Economedia.roFinanciarul.ro+4Sep 2026German court rules Meta liable for scam and harmful ads on platformsConfirmedFraud, scams and manipulationThe Next WebSep 2026AI Deepfake Video Impersonates Tamil Nadu CM in Financial ScamUnder investigationFraud, scams and manipulationAsianet News Network Pvt LtdAsian News International (ANI)Deccan Chronicle+5Aug 2026Poland Seeks €250 Million EU Fine Against Meta Over Fraudulent AdsUnder investigationFraud, scams and manipulationReuterswww.theepochtimes.comAug 2026AI-generated deepfake of Polish President in investment scam on Meta platformsUnder investigationFraud, scams and manipulationHaberlerCNN TΓΌrk