Observed Signal · Apr 27, 2026 · Regulatory Report · Source: techcrunch · Impact: 4/5 · Sentiment: Negative
FTC: Consumers Lost $2.1B to Social Media Scams (2025)
The U.S. Federal Trade Commission reported that Americans lost $2.1 billion to social media scams in 2025, an eightfold increase year-over-year. Nearly 30% of people who reported monetary losses said the schemes began on social platforms, with Facebook generating higher reported losses than any other platform; WhatsApp and Instagram ranked second and third. Shopping scams were the most-reported social-media scam type (over 40% of victims ordered items seen in ads), investment-related scams accounted for $1.1 billion in losses, and about 60% of reported romance scams started on social platforms. The FTC urged users to limit profile visibility, vet online sellers and advisers, and avoid letting online acquaintances direct investment decisions.
A major FTC report quantifies large and rapidly rising consumer losses tied to social platforms, highlighting systemic fraud risks that affect trust, ad safety, and may prompt regulatory scrutiny or platform policy changes.
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Key Takeaways & Evidence Grounding
- FTC report: Americans lost $2.1 billion to social media scams in 2025.
- Losses from social media scams increased eightfold year-over-year.
- Nearly 30% of reported scam losses began on social media platforms.
- Facebook accounted for more reported losses than any other social platform; WhatsApp and Instagram followed.
- Investment scams originating on social platforms caused $1.1 billion in losses; shopping scams were the most reported type.
Connected Companies & Entities
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Study: Deepfake Voice Scams Could Cost Americans $40B
An Insuranceopedia analysis estimates deepfake voice scams could cost Americans about $39.7 billion per year in a base-case scenario, using FTC median loss figures and surveys showing widespread exposure. The study cites research from Hiya, McAfee, the FTC, and U.S. Census data, and reports that roughly 25% of Americans received a deepfake voice call in the past year and that 77% of those victims reported losing money. Regulators and platforms are responding: the FCC adopted strengthened Know-Your-Customer rules for voice providers and proposed additional rules to hold providers accountable, while platforms have expanded likeness-detection tools. The article provides consumer tips to verify callers and avoid rapid-payment scams.
Account Fraud and Phishing Surge in 2025
A t3n report highlights a marked rise in account- and card-related fraud, driven by phishing and smishing attacks that increasingly use AI to personalize messages. Germany’s police statistics (PKS) recorded about 96,400 cases around accounts and payment cards in 2025, a ~5% increase year-on-year, and total losses reached roughly €2.25 billion (up ~65%). Detection and prosecution rates remain low: only about 20% of cases overall led to suspects being identified, and cases likely originating abroad had an investigation rate of just 1.4%. Consumer advocates say banks often refuse reimbursements by alleging gross negligence; the Verbraucherzentrale Bundesverband (vzbv) reported a 54% rise in cybercrime complaints related to financial services. Authorities warn the true number of incidents is much higher because many victims do not file reports.
Kick Chat Scraper Archives Live Chat via Pusher
A developer has published an Apify Actor that archives live chat from Kick.com by connecting to the same public Pusher WebSocket the Kick frontend uses. Kick exposes no API or VOD-attached replay for past chat, so this scraper captures messages in real time (sender, role, badges, color, timestamps) while a stream is live. The Actor (DevilScrapes/kick-chat-archive) validates and writes one typed row per message, charges $0.001 per message plus $0.05 per run, and includes run controls for channel slugs, duration, and message caps. The post explains technical challenges (Cloudflare-protected channel lookup requiring browser TLS fingerprinting and proxy rotation, Pusher connection reliability, role derivation from badge arrays), use cases (moderation audits, brand monitoring, model training), limitations (real-time only, public rooms only, moderation events intentionally dropped), and how to run the Actor via Apify.
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