Amazon deleted 20,000 suspicious reviews following Financial Times investigation

Amazon deleted 20,000 suspicious reviews following Financial Times investigation

The FINANCIAL -- Amazon’s top reviewers in the UK appear to have engaged in fraud, leaving thousands of five-star ratings in exchange for money or free products. The company took down 20,000 product reviews following an investigation by the Financial Times.

Justin Fryer, the number one Amazon reviewer in the UK, left a five-star rating once every four hours on average in August, according to the FT’s analysis. Many of these reviews were for products from random Chinese companies. Fryer then seems to have resold the products on eBay.

A FIVE-STAR RATING ONCE EVERY FOUR HOURS ON AVERAGE

According to `the VERGE reports, scams like these typically start on social networks and messaging apps such as Telegram, where companies can meet potential reviewers. Once the connection is made, the reviewer chooses a free product, then waits a few days to write a five-star review. After the review is posted, they get a full refund, and, at times, an extra payment.

Amazon has a specific rule against posting reviews in exchange for “compensation of any kind (including free or discounted products) or on behalf of anyone else.” But nine of the 10 top reviewers in the UK seem to have broken that guideline, engaging in suspicious activity. The 20,000 reviews that were removed were written by seven of the top 10 reviewers.

The company was alerted to Fryer’s activity in early August. At least one Amazon user reported the man’s questionable ratings to CEO Jeff Bezos. This user was told the company would investigate, although it failed to take action until today.

Fryer maintains that he definitely did not get paid to post fake five-star ratings, and he says that his eBay listings for “unused” and “unopened” products were extras, according to the Times.

What is Amazon Fraud Detector?

Amazon Fraud Detector is a fully managed service that makes it easy to identify potentially fraudulent online activities such as online payment fraud and the creation of fake accounts.

Did you know that each year, tens of billions of dollars are lost to online fraud world-wide?

Companies with online businesses have to constantly be on guard for fraudulent activity such as fake accounts and payments made with stolen credit cards. One way they try to identify fraudsters is by using fraud detection apps, some of which use Machine Learning (ML).

Enter Amazon Fraud Detector! It uses your data, ML, and more than 20 years of fraud detection expertise from Amazon to automatically identify potentially fraudulent online activity so you can catch more fraud faster. You can create a fraud detection model with just a few clicks and no prior ML experience because Fraud Detector handles all of the ML heavy lifting for you.

How it works..

“But how does it work?” you ask. 🤷🏻‍♀️

I’m so glad you asked! Let’s summarize this into 5 main steps. 👩🏻‍💻

Step 1: Define the event you want to assess for fraud.
Step 2: Upload your historical event dataset to Amazon S3 and select a fraud detection model type.
Step 3: Amazon Fraud Detector uses your historical data as input to build a custom model. The service automatically inspects and enriches data, performs feature engineering, selects algorithms, trains and tunes your model, and hosts the model.
Step 4: Create rules to either accept, review, or collect more information based on model predictions.
Step 5: Call the Amazon Fraud Detector API from your online application to receive real-time fraud predictions and take action based on your configured detection rules. (Example: an ecommerce application can send an email and IP address and receive a fraud score as well as the output from your rule (e.g., review))

Author: The FINANCIAL