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The world of online reviews saw a major shift last year. Effective as of October 2024, the U.S. Federal Trade Commission (FTC) finalized a rule that holds e-commerce businesses accountable for every review on their site.
In other words, if a fake review appears on your site, your company could face penalties of up to $52,000 per violation. For brands and platforms that rely on customer trust to drive conversions, this rule means that having fake reviews on your site doesn’t just put your reputation at risk — the risks are also legal and financial. Especially with the widespread adoption and use of AI tools to generate fake reviews en masse.
Let’s dive into what the rule says and what it means for e-commerce companies.
What the FTC’s fake review rule actually covers
The FTC’s rule leaves little room for interpretation. It bans the creation, purchase, and dissemination of fake consumer reviews and testimonials across the e-commerce ecosystem. This means merchants and platforms that host or syndicate reviews are responsible for fraudulent content that appears on their watch — making it essential to know how to fight review fraud.
The rule addresses a range of deceptive practices, including:
- Writing or paying for fake reviews or testimonials, including AI-generated fake reviews
- Using or spreading undisclosed insider reviews or testimonials by a company’s officers, managers, employees, or agents
- Attempting to suppress negative consumer reviews through threats, intimidation, or false claims
- Misrepresenting that displayed reviews reflect all feedback when negative reviews are hidden
With fines reaching $52,000 per fake review, non-compliance can quickly become a serious financial liability, especially for platforms managing reviews at scale.
Generative AI: The new favorite tool for review fraud
AI tools can now generate reviews that are nearly indistinguishable from those written by real customers. They mimic tone, nuance, and structure, making it incredibly difficult for shoppers (and even many fraud filters) to spot the difference.
Legacy detection methods, which once flagged repetitive language or awkward phrasing, are no longer enough. AI-generated reviews vary their tone and vocabulary, evade pattern-matching algorithms, and blend seamlessly with legitimate feedback. This flood of authentic-looking fake content undermines trust and exposes businesses to regulatory risk.
When review fraud hits home: A real-world platform’s struggle
Our team recently spoke with a major reviews platform that syndicates e-commerce reviews for global brands. This company shared that merchants have been submitting large numbers of AI-generated reviews to boost product ratings. The platform’s team could catch some fakes using external AI detectors and by flagging obvious anomalies, such as mismatched usernames and emails or suspicious IP addresses, but these manual methods were labor-intensive and incomplete.
The volume and sophistication of the problem quickly outpaced their ability to respond. Enterprise partners raised concerns about the integrity of these reviews, and with the FTC’s ban in place, every missed fake review now carried reputational risk and the threat of severe financial penalties. The business faced a stark reality: Without a better solution to detect and stop fake reviews, it risked losing both key partnerships and revenue.
Case study: How one company stopped review fraud with Fingerprint
How device intelligence can help spot review fraud
Device intelligence platforms offer a more reliable and persistent way to identify the source of each review, regardless of how authentic the text may appear. For example, Fingerprint uses more than 100 real-time browser, network, and device signals to generate a unique, persistent visitor ID for every device that submits a review. The visitor ID remains consistent even if the user clears cookies, switches to incognito mode, or connects through a VPN.
By tying every review submission to a unique visitor ID, Fingerprint can expose clusters of fraudulent activity, such as when a single device submits multiple reviews under different accounts. Device intelligence can help uncover patterns that would otherwise remain hidden, such as a merchant boosting their own products or a fraudster orchestrating a coordinated campaign of fake testimonials to make their phony products seem authentic.
How Fingerprint makes AI-generated review fraud visible
Identification is only the beginning. Fingerprint also provides over 20 Smart Signals that flag suspicious behaviors in real time. For review platforms, Smart Signals such as Bot Detection and Browser Tampering Detection are especially valuable because they can reveal when an automated browser submits a review or when someone is trying to conceal their true identity. When used in conjunction with the Suspect Score, which provides a numerical value to help you assess the level of risk for a visitor ID, Fingerprint can help you spot and block suspicious devices more quickly.
It’s time to get proactive about review fraud
Generative AI has made fake reviews nearly impossible to spot with traditional methods, and the FTC’s crackdown means the risks tied to hosting fake reviews can have outsized regulatory and financial impact. Implementing a device intelligence platform like Fingerprint helps e-commerce businesses mitigate the number of fake reviews on their sites by detecting and linking reviews originating from the same device, even when users try to mask their identity with VPNs, proxies, or device spoofing.
If you want to strengthen your review verification process and protect your business from costly penalties and reputational damage, we’re here to help. Learn more about Fingerprint’s device intelligence or reach out to our team for a personalized demo.
