Recent survey findings indicate cyber-enabled fraud is increasingly affecting small organizations and workers, with measurable business impact. The Identity Theft Resource Center reported that 80% of small businesses experienced a cyberscam or breach in the prior 12 months, and 38% of impacted firms said they passed losses to customers by raising prices; the report also noted that a significant portion of these scams were AI-fueled, and that repeat victimization (multiple breaches within a year) was common.
Separately, a TransUnion study of U.S. gig workers found fraud is routine on gig platforms, with 34% of respondents reporting they had been defrauded by consumers through issues such as payment disputes, chargebacks, and “tip baiting.” The study also highlighted security and trust risks created by worker responses to fraud, including participation in prohibited practices like account renting/selling, and pointed to demand for stronger platform controls such as identity verification, address validation, and enforcement against repeat offenders—underscoring gaps between deployed safety features and user confidence in protections.

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Follow-on reporting on the same ITRC survey said 41% of small-business victims in 2025 attributed a recent attack's root cause to AI, indicating a sharp rise in AI-enabled cybercrime compared with 2024. The report also noted declining MFA adoption, large per-incident losses, and growing difficulty obtaining cyber insurance after breaches.
An Identity Theft Resource Center survey reported that four in five small businesses experienced a cyberscam or security/data breach during 2025, with many suffering multiple incidents. The survey found that 38% of affected businesses raised prices to offset losses, showing direct economic impact on customers.
A TransUnion study published in January 2026 reported widespread fraud and abuse affecting U.S. gig workers, including payment disputes, chargebacks, tip baiting, fake users, and fake listings. The study also found that account renting and selling were commonly observed, allowing unverified individuals to bypass background checks and weakening platform trust and attribution.
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