How well do humans spot fake reviews on e-commerce platforms? Not well at all according to a recent study conducted by Cornell University. The study found that humans suffer from what is known as a “truth bias”. This means that we as humans assume that what we read is true until proven otherwise with substantial evidence.
On the other end of the spectrum, when humans are specifically trained at detecting deception, they can become overly skeptical. This means that their detections of fraud can occur too often, resulting in severe inaccuracies. So it’s evident that humans aren’t exactly the most reliable detectors of fraud as bias can influence both ends of this spectrum.
Cornell decided to use Artificial Intelligence (A.I.) to detect false reviews on hotel sites which resulted in a whopping 90% accuracy. Three key findings resulted from this experiment:
E-commerce customers can use these three key findings to make their own judgements when it comes to online reviews on e-commerce sites. However, what if this needed to be conducted on a much larger scale? For example, if an e-commerce site has an abundance of product listings, there will more than likely be plenty of reviews of those products. Is the use of human detectors a practical way for online marketplaces to find these fake reviews? Probably not. A.I. is much more efficient when it comes to cost, resources, and speed, and most importantly: accuracy.
Now that we have a stronger understanding of what constitutes a fake review, you may be thinking: do false reviews really make that much of an impact when it comes to returning customers? Well the short answer is: yes. In fact, 90% of shoppers’ buying decisions are influenced by online reviews. So fake reviews can be extremely damaging to a brand’s reputation as they completely betray any loyalty that brand worked to create between them and their customers.
What brands and e-commerce sites may not be aware of is how much weight reviews carry when it comes to trust among their customers. 95% of consumers suspect censorship or false reviews are at play when bad reviews are nowhere to be found online. Skepticism in e-commerce, much like almost every place on the internet, is inevitable to an extent. However, according to Quartz India, 56% of online shoppers don’t trust product reviews, 65% don’t trust product ratings, and a whopping 72% of Indian e-commerce buyers are weary of incorrect information and believe fake reviews have become the norm. Is this really the right direction for e-commerce to be moving in?
Online shoppers can take matters into their own hands and decide for themselves whether or not a review of a product is fake. However, it is primarily the e-commerce site’s responsibility to ensure that its customers do not have to do this. Of course, brands shouldn’t be writing false reviews of their products in the first place, but until there is a way to stop them, elimination will win out over prevention.
So what can e-commerce sites and online marketplaces do to protect their users? Using A.I. to detect and eliminate fake reviews on their sites is the most accurate way for online marketplaces to combat the scourge of fraud and deception that are rife online. A good example would be e-commerce giant, Amazon, who has already taken matters into their own hands. They have adopted an A.I. machine learning system that they built in-house to boost the weight of verified and newer customer purchase reviews and reviews that are deemed helpful by other users. This is just one way Artificial Intelligence can detect and weed out fake reviews in online retail.
LogoGrab specializes in logo detection technology used by leading e-commerce platforms to protect the legitimate brands that sell products on their sites against counterfeiters. This is slightly different to the A.I. technology used by Amazon to detect fake reviews, but it is just another way in which Artificial Intelligence can be used to eliminate fraud in e-commerce.
So A.I. can be used across e-commerce platforms for both Fraud Prevention and Brand Protection. Click the link to learn more about our Counterfeit Detection Visual-AI solution.
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