How to Prevent Fake Reviews with Email and Phone Intelligence

Online reviews shape how businesses are perceived, influence purchasing decisions, and serve as a critical component of digital trust. Whether consumers are choosing a product, subscribing to a service, or booking a professional consultation, they turn to reviews to validate their choices. Businesses, in turn, rely on these ratings to establish credibility and attract customers.

However, fake reviews have become an epidemic. The rise of automated bots, incentivized review farms, and AI-generated feedback has made it increasingly difficult to distinguish genuine reviews from fraudulent ones. For businesses, the consequences are severe—fake reviews distort reputations, mislead consumers, and ultimately erode trust in entire digital services. Review platforms that fail to filter out fraudulent feedback risk losing credibility, facing regulatory scrutiny, and suffering financial damage.

This article explores the scale of the fake review problem, the techniques fraudsters use, and how businesses across industries can leverage identity intelligence to detect and prevent fake reviews effectively.

How big is the fake review problem?

Fake reviews aren’t just an occasional nuisance; they represent a multi-billion-dollar issue affecting businesses across various industries. Online marketplaces, SaaS providers, e-commerce platforms, digital subscription services, and especially professional review sites all face the challenge of distinguishing real customer feedback from fabricated content.

Also, according to the Washington Post's findings, between 30-40% of reviews online are inauthentic or fake; with fraudsters using bulk-created accounts, paid reviewers, and AI-generated content to manipulate ratings. This affects everything from product sales and app store rankings to B2B service credibility and local business reputation scores. To combat review fraud, the FTC enacted a rule in August 2024 that makes it illegal for businesses to create or display false reviews. The e-commerce market is not doing too well either. In 2024, 49% of U.S. consumers were confident in having experienced fake product reviews on Amazon (source: Statista). 

The impact of fake reviews is twofold. Consumers are misled into making poor purchasing decisions, often resulting in increased refund requests, negative word-of-mouth, and damaged brand trust. Meanwhile, businesses find themselves competing against fraudulent actors who artificially boost their visibility, making it harder for genuine companies to reach their target audience.

Fake reviews don’t appear randomly. Many are generated using compromised accounts, synthetic identities, and disposable contact details, allowing fraudsters to bypass platform verification. One of the most well-known fraud tactics contributing to this issue is order brushing—a deceptive strategy designed to inflate ratings artificially.

What is order brushing and how does it fuel fake reviews?

One of the most sophisticated ways fraudsters generate fake reviews is through order brushing. This is a practice where businesses—or fraudulent actors hired by businesses—place fake orders to create a false transaction history and generate positive reviews that manipulate rankings and credibility scores.

Here’s how it works: fraudsters use fabricated identities, stolen data, or disposable email and phone numbers to make it appear as though real customers are making purchases or using services. Once the fake transaction is complete, an automated or paid reviewer leaves a five-star review, inflating the seller’s reputation. While this practice is most common in e-commerce, it has also spread to digital services, professional review platforms, and software marketplaces where businesses depend on customer ratings.

Order brushing is particularly harmful because it creates an illusion of popularity. Consumers trust highly rated products and services, leading them to engage with businesses that may not be delivering real value. Meanwhile, legitimate businesses that follow ethical practices find themselves outranked by competitors using fraudulent tactics.

Since order brushing operates at scale, manual detection is ineffective. Fraudsters can create thousands of fake transactions using burner emails, disposable phone numbers, and accounts with no real digital footprint. To combat this, businesses can turn to identity intelligence solutions that analyze email and phone data to flag high-risk reviewer accounts before they can manipulate platforms.

The regulatory crackdown: the EU Omnibus Directive and other laws

As the problem of fake reviews grows, regulatory bodies are introducing stricter consumer protection laws to ensure transparency in online ratings. One of the most significant developments is the EU Omnibus Directive, which mandates that platforms verify the authenticity of reviews. Businesses that fail to comply risk facing fines and reputational damage.

The directive, which took full effect in Italy in early 2024, requires companies to explicitly disclose their review verification processes. If businesses cannot prove that customer reviews are genuine, they could be found in violation of consumer protection laws. The goal is to prevent platforms from misleading consumers by displaying unverified or fraudulent reviews.

Outside the EU, other countries are following suit. The United States Federal Trade Commission (FTC) has increased enforcement actions against businesses that engage in review manipulation. Companies caught fabricating reviews or failing to remove fraudulent feedback have faced multi-million-dollar penalties.

These regulatory pressures mean businesses must take proactive steps to ensure the integrity of their review systems. Relying on self-reported user feedback is no longer sufficient—companies need automated solutions that analyze email and phone data to detect fraudulent reviewer activity in real-time.

How can email and phone intelligence help detect fake reviews?

Fake reviews thrive because many platforms lack effective identity verification measures. Fraudsters exploit this by using disposable email addresses, virtual phone numbers, and synthetic identities to bypass security checks and create seemingly legitimate reviewer accounts. This is why email and phone intelligence play a critical role in detecting and preventing fraudulent activity at scale.

Carefully interpreting the information gathered from the digital footprint of an email or phone number, businesses can identify suspicious accounts before they have the chance to leave fake reviews. Some of the key ways identity intelligence helps include:

• Detecting newly created accounts: fraudulent reviewers often use freshly registered emails and phone numbers, which lack a history of real activity. Identifying these early helps block fake accounts before they gain credibility.
• Flagging disposable and temporary contacts: many fraud operations rely on burner email services and virtual phone numbers, allowing them to bypass verification. A strong detection system can identify these high-risk contact details instantly.
• Checking online presence and digital history: a legitimate reviewer is likely to have a digital footprint, whether through social profiles, professional accounts, or past transactions. Reviewers with no online presence may be bots or fraudulent actors.
• Monitoring data breach exposure: if an email or phone number appears in multiple data breaches, it could indicate compromised credentials being used for fraudulent activities, including review manipulation.

Unlike traditional fraud detection, which relies on manual moderation or behavioral analysis, email and phone intelligence work at the identity level—allowing platforms to stop fake reviewers before they can leave misleading feedback. In the next section, we’ll break down the specific email and phone signals businesses can use to verify reviewer authenticity.

Key email and phone signals to detect fake reviewers

Fraudulent reviewers leave behind digital traces, and by analyzing these signals, businesses can identify and filter out suspicious accounts before they leave misleading feedback. The most effective way to do this is through email and phone intelligence, which helps detect inconsistencies, patterns, and risk factors associated with a reviewer’s identity.

Email Intelligence Signals

An email address can reveal a great deal about a user’s authenticity. Here are some of the most critical email-based fraud detection signals:

• Email age & first seen date
  A newly created email address is a red flag, as fake reviewers often use fresh accounts to bypass security checks. If an email was created just days or weeks before the review, it warrants further scrutiny.
• Online presence & digital footprint
  Legitimate reviewers typically have some level of online activity linked to their email. If an email has zero associated social media or online profiles, it may belong to a synthetic identity.
• Disposable or temporary email detection
  Many fake review schemes use burner emails to create multiple fraudulent accounts. Identifying disposable or temporary domains can help platforms flag non-trustworthy accounts.
• Email-to-name & email-to-phone matching
  A legitimate reviewer should have consistent identity information across multiple platforms. If an email cannot be linked to a real name or phone number, it’s often a sign of fraud.
• Data breach exposure
  An email appearing in multiple data breaches suggests it has been compromised and could be used in fraud networks, including review manipulation rings.

Phone Intelligence Signals

Phone numbers are another key identifier in fraud detection, offering businesses additional layers of verification. Here are some high-risk indicators when analyzing a reviewer’s phone number:

• Phone number age & porting history
  Fraudsters often use newly activated numbers or frequently ported numbers to mask their identity. A number that has been ported from a standard carrier to a no-KYC one could present higher risk.
• Carrier type (prepaid vs. no-KYC SIMs)
  Many fraudsters rely on prepaid or anonymous SIM cards that do not require identity verification. Detecting whether a number is associated with a no-KYC carrier helps assess risk.
• Disposable or virtual numbers
  Just like disposable emails, virtual phone numbers allow fraudsters to create multiple fake accounts without needing a real phone. Businesses can block or flag reviews from numbers that belong to temporary phone services.
• Phone-to-name & phone-to-email matching
  A real customer’s phone number will likely be linked to their email and online accounts. If a phone number has no connections to a legitimate digital identity, it may belong to a fraudulent reviewer.
• Messaging & online presence
  A highly active phone number linked to multiple messaging apps, marketplaces, or social media accounts suggests a real user, whereas a number with no digital footprint may indicate fraud.

Companies can utilize an amalgamation of email and phone intelligence to effectively enhance their review verification processes, reduce fraud, and protect their reputation from misleading feedback. In the next section, we’ll explore how Trustfull’s technology helps streamline these processes without disrupting the customer experience.

How Trustfull strengthens fake review detection

Detecting fake reviews at scale requires more than manual moderation or traditional fraud prevention methods. Fraudsters continuously adapt their tactics, using burner emails, disposable phone numbers, and synthetic identities to bypass basic verification processes. This is where identity intelligence comes into play—offering businesses the ability to analyze digital signals tied to email and phone data to detect fraudulent reviewers before they can manipulate ratings.

There’s a wide variety of benefits to be unlocked by analyzing multiple identity signals. Some of these benefits include:

• Verify if an email or phone number has a history of real activity – Trustfull helps determine whether the contact details associated with a reviewer are linked to real, long-term usage.
• Detect high-risk emails and phone numbers before fake reviews go live – Temporary or disposable email addresses and virtual phone numbers are often used in fraud networks. Trustfull’s system can automatically flag these before they become a problem.
• Identify synthetic identities behind fake reviews – this can be accomplished after methodically cross-referencing email and phone data; businesses can assess whether the reviewer’s digital footprint aligns with a genuine identity.
• Assess online presence and account consistency – Trustfull detects if a reviewer’s contact information is tied to active, legitimate online profiles, helping platforms determine if the reviewer's identity is authentic.

Trustfull provides real-time identity insights that help businesses verify whether a reviewer is using a legitimate, long-standing identity or a fabricated one created solely for manipulation.

Make sure you stick around and find out how to stop the negative impact of fake reviews by having a quick chat with our fraud prevention team.