Online reviews used to feel like quick notes from real people.
Now, they can also come from paid writers, organized networks, or AI tools.
This article shows how to spot fake and AI-generated reviews in a practical, step-by-step way.
It is educational only and does not tell you what to buy, avoid, or invest in.
For a broader guide to checking companies before you pay, see:
How to Use BBB and Review Sites Before You Send Money.
Why fake and AI reviews are growing
The U.S. Federal Trade Commission (FTC) now has a specific rule banning fake reviews and testimonials. It prohibits selling or buying fake reviews, including those written by people who never used the product or generated by AI, and allows civil penalties per violation.
Legal and compliance analyses of the rule note that it targets:
- Reviews written by non-customers
- Reviews bought or sold in bulk
- Company-controlled “review sites” that pretend to be independent
Despite this, fake reviews still appear because they work:
- One 2025 overview of fake review statistics reports that fake positive reviews can increase sales in the short term and that even one extra fraudulent star can lift demand noticeably.
- Research and watchdog data show a sharp rise in AI-generated reviews since mid-2023, especially in app stores.
So the rule tries to deter abuse, but it does not remove your need to read reviews with care.
What AI-generated fake reviews tend to look like
AI can now write reviews that sound natural and polite.
A 2025 study comparing AI reviews and human reviews found that AI-generated fakes often show:
- High fluency – the text reads smoothly, with few grammar errors
- Low specificity – little detail about how, when, or where the product was used
- More exaggeration – very strong praise or very strong criticism
- Less nuance – fewer small complaints or mixed opinions
Watchdog reports on app stores describe patterns such as:
- Many short 5-star reviews arriving in a cluster
- Similar tone and structure across dozens of comments
- Praise that looks like a product description, not a personal experience
In other words, AI reviews often feel smooth but generic.
Step 1: Consider the source, not just the stars
The FTC’s guide on evaluating online reviews suggests a basic starting point: consider the source and ask questions.
You can apply that by asking:
- Where is this review posted?
- A large marketplace? The company’s own site? A third-party platform?
- Is the platform known for moderating reviews or verifying purchases?
- Does the site earn money when I buy this product?
The FTC also reminds consumers that some sites get paid when people click through and buy, which can affect how reviews and rankings appear.
Stars alone do not tell you who wrote the review or why it is highlighted.
Step 2: Look for patterns in timing and volume
Fake and AI-generated reviews often show strange timing patterns.
Several consumer and industry guides suggest checking:
- Review spikes – Did dozens of 5-star reviews appear in a few days after a long quiet period?
- Gaps – Was the product quiet for months, then suddenly “perfect” according to many reviewers?
- Update timing – Did scores jump right after a major sale or marketing campaign?
A 2025 review-fraud report notes that sudden bursts of glowing reviews can raise conversion quickly, which is why dishonest sellers pay for them.
A more natural pattern often shows:
- Reviews spread over time
- A mix of positive and negative experiences
- Score changes that look gradual, not instant
You do not need to build charts.
Just scroll through the dates and see if the pattern feels organic or forced.
Step 3: Read the language closely
Both the FTC and independent reputation experts suggest reading the actual text, not just the rating.
Possible red flags:
- Overly general praise
- “Amazing product!!! Best ever!!! Highly recommend!!!” repeated many times
- No mention of how the item was used or for how long
- Marketing-style phrases
- Sentences that sound like ad copy or repeat the product description
- Unusual technical terms copied from the listing
- No small imperfections
- Real users often mention at least one minor issue or trade-off
- All-perfect, no-details language across many reviews can be a warning sign
- Similar length and structure
- Many reviews with the same number of sentences and similar rhythm
- Recycled phrases or identical sentence openings
A recent large-scale analysis of AI fake reviews found exactly these traits: high readability but lower specificity and a tendency toward clichés and generic praise.
You can ask yourself:
“Does this sound like someone telling a friend what happened,
or like someone trying to impress an algorithm?”
Step 4: Check the reviewer profile
The Better Business Bureau recommends looking at who is leaving the review, not only what they wrote.
On many platforms you can click the name and see:
- Profile name
- Very generic names like “John Smith” or random letters and numbers can be red flags, especially if repeated.
- Profile details
- No picture, no other information, and a strange username by itself is not proof, but it adds context.
- Review history
- If the same user posts only 5-star reviews for many unrelated products in a short time, that can look suspicious.
Reputation and fraud-detection firms suggest scanning reviewer histories when possible. Clusters of similar 5-star posts across many brands may point to paid or automated activity.
Again, one odd profile does not prove anything.
But many weak profiles behind key reviews deserve attention.
Step 5: Compare extremes and middle-range reviews
Some practical guides recommend reading:
- A few 5-star reviews
- A few 1- and 2-star reviews
- Several 3- and 4-star reviews
Why?
- Extreme reviews (very positive or very negative) can be more emotional, fake, or both.
- Middle-range reviews often include balanced pros and cons and concrete details.
A recent article warning about fake 5-star reviews in the UK noted that scammers often rely on vague, hyper-positive language, while genuine feedback tends to include specific examples and imperfect experiences.
Patterns to notice:
- Do 3- and 4-star reviews share similar small issues?
- Do negative reviews repeat the same serious concern?
- Do the positive reviews answer those concerns with facts, or just with generic praise?
This comparison helps you see themes, not just single opinions.
Step 6: Be extra cautious with apps and financial products
Fake and AI-generated reviews are not limited to gadgets or home items.
Recent reports show a surge in AI-powered fake reviews in app stores, including finance apps.
Some investigations highlight:
- Fraudulent apps that use fake reviews to climb rankings
- Apps that flood stores with AI-written praise to hide serious issues
- Cases where apps with glowing reviews later turned out to misuse data or bombard users with ads
When reviews relate to money, investing, trading, or “passive income”:
- Treat very bold claims with extra care.
- Pay attention to complaints about withdrawals, access to funds, or sudden charges.
- Cross-check the company on BBB, on official regulatory sites if relevant, and through independent sources.
Step 7: Cross-check with BBB, Scam Tracker, and other sources
Reviews are only one layer.
You can combine them with other checks:
- BBB business profiles and ratings
- BBB Scam Tracker entries for similar names, URLs, or phone numbers
- FTC Consumer Advice pages on evaluating reviews and avoiding scams
If reviews look glowing but:
- BBB shows many unresolved complaints, or
- Scam Tracker lists similar stories of non-delivery or misrepresentation,
you have a reason to slow down and gather more information.
For a full step-by-step process using these tools, see:
How to Use BBB and Review Sites Before You Send Money.
A short mental checklist before you trust reviews
You do not need a spreadsheet.
You can keep a simple checklist in mind:
- Source – Where am I reading this review? Who benefits if I buy?
- Timing – Are there sudden bursts of similar 5-star reviews?
- Language – Does the text sound like a real experience or like a short ad?
- Profile – Does the reviewer have a history that looks human and varied?
- Balance – Do mid-range reviews share consistent pros and cons?
- Cross-check – What do BBB, Scam Tracker, and independent sources say?
You can still decide to buy—or not buy—after that.
The point is to see more than the star count before money leaves your account.
Conclusion
AI and fake reviews now shape what people see when they shop, download apps, or consider financial services.
Studies show that AI-generated reviews tend to be fluent, generic, and less specific, while regulatory action from the FTC and guidance from the BBB and other experts confirm that fake reviews remain a real and growing problem, even under new rules that ban them.
For everyday consumers, the most practical response is not to panic, but to read more carefully: check the source, timing, language, and reviewer profile, compare extremes with middle-range opinions, and cross-check what you find with tools like BBB and Scam Tracker and the broader educational content on saveurs.xyz.