About the company

An EU-based company that provides a peer-to-peer marketplace for selling online services. The company's website is popular across Europe, with over 3 million monthly unique visitors in Italy alone.

The challenge

The company was struggling with fake reviews on their marketplace. Many users on their site would sort listings based on total number of reviews and average star rating in order to surface the highest quality services. Due to this, vendors were incentivized to collect as many reviews as possible, and many would try to inflate their ratings with fake reviews. These vendors would either pay for services that would submit multiple fake positive reviews, or would write multiple reviews themselves by creating multiple accounts. The company wanted to ensure that vendors with high ratings were authentically loved by the community, and not merely the most successful at circumventing review limits.

The company found that many fake reviews were being created from the same device, but the perpetrator would use a VPN or clear cookies to conceal their identity. The team decided that the best solution to their fake review problem was a service that could accurately identify visitors even if they changed their IP address or used other concealment methods.

Solution Overview

A developer at the company found out about Fingerprint after seeing a post on Stack Overflow about identifying anonymous visitors for the purposes of fraud prevention.

The company initially used Fingerprint' open source library for browser fingerprinting, then decided to trial Fingerprint for increased identification accuracy.

After investigation, the company found that the Fingerprint API was able to catch a significant number of fake reviews, improving the integrity of their review system and overall customer experience.

How the company caught fake reviews

The company was already checking whether an account was being created on an email or IP address that had previously created an account, so it was easy for engineering to incorporate Fingerprint's VisitorIDs as an additional check on account creation. Every time a user attempted to submit a review, the company would check whether that visitorID had previously submitted a review associated with the specific service (or serviceID). If they had, the user would be blocked from submitting another review.

Using this methodology the company was able to catch 500 additional fake reviews a day, making a large dent in their review fraud problem with this one simple solution.

Future growth into other fraud prevention use cases

While the company has been focused on fake review prevention thus far, they plan to use Fingerprint for other fraud use cases in the future due to its flexibility. One of the main future use cases for Fingerprint is preventing referral fraud and new account signup fraud, as new customers are incentivized with significant discounts. Another possible use case is in preventing card cracking, card testing and other forms of payment fraud to reduce revenue losses from chargebacks.

Key takeaways

With Fingerprint, the company effectively tackled its fake review problem, ensuring that high-rated vendors earned their status through genuine community feedback. By accurately identifying visitors — even those using VPNs or clearing cookies — the company blocked 500 additional fake reviews daily, boosting the credibility of their review system and improving the customer experience. Looking forward, the company plans to expand Fingerprint usage to prevent referral fraud, account signup fraud, and payment fraud, supporting a more secure and trustworthy marketplace as they grow.