If there’s one thing we know, it’s that fraud comes in many unsavory flavors. From purchases made with stolen credit cards to phishing schemes, fraudsters are always looking for new and novel approaches to scamming victims.
There are a variety of reasons someone might create a fake profile on a dating site, from the curious (“I wonder if anyone would respond to someone like this?”) to the insecure (“What if I looked like this instead?”) to the downright criminal. Sometimes, fake profiles are set up by organized crime rings who use bots to send phony messages and coax victims into parting with their money.
Romance scams are a huge, costly, and disturbing problem. According to the FBI, romance scams cost victims more than $82 million in the last six months of 2014 alone, with the average victim losing more than $100,000. Yes, that’s five zeros. Ouch.
For the dating sites that host these fake profiles, the problem can also come with damaging consequences. Their brand reputations are at stake. User experience suffers. And internal teams often find themselves devoting more time than they’d like to identifying and dealing with these pests, which – despite the company’s best efforts – keep popping up again and again.
Scope of the problem
We’ve already learned that romance scams – however they’re perpetrated – can be costly. But how rampant are fake dating profiles? We analyzed a sampling of more than 8 million profiles created in the past year on dating sites that use Sift to find out how many phony profiles had been blocked during that period.
The results? We discovered that 10% of all new dating profiles created were fake. We also found that:
Male profiles are 21% more likely to be fake than female profiles
The most common age listed on fake profiles is 36
However, users listing their age as 64 had the highest fraud rate. One factor contributing to this is the relatively small number of dating site users in this age group.
Location, location, location
Location is common signal used, in conjunction with other clues, to determine whether a user is a fraudster. So, what about dating site users? Typically, location is determined via shipping, billing, or IP address – but in this case, we took the location directly from what someone had filled in on their profile.
When looking at where the “users” in these profiles hailed from, we discovered that Nigeria, Ghana, the Netherlands, Romania, and South Africa had the highest fraud rates. Surprised? Most people are familiar with Nigeria’s reputation for email scams. However, we don’t recommend blocking users based on a single factor like country – even if it’s appearing at the top of our list. Creating rules like this is far too black-and-white to effectively deal with something as nuanced as fraud, and you run the risk of inadvertently blocking good users.
Fighting fakes at scale
That’s why dating sites – and other sites in which users create profiles, like social networks, marketplaces, and job sites – often turn to a machine learning-based solution to help automate the discovery of phony profiles. While many of Sift’s customers use us to reduce chargebacks, a significant segment are more focused on weeding out fake users and profiles before they actually harm their legitimate customers.
Our algorithms process a variety of potential fraud signals, both industry standard (like IP address, account age, location, etc.) and custom data chosen by the individual site (like, say, whether someone has uploaded a profile photo) to identify the profiles most likely to be phony before an unsuspecting person has a chance to get conned.
The good news is that the profiles we looked into never saw the light of day, since they were preemptively blocked or deleted after being flagged as phony. Still, users of dating sites should – as always – stay vigilant and practice healthy skepticism.
Interested in learning how Sift helps dating sites fight fake profiles and fraud? Check out our Zoosk case study!
Stop fraud, break down data silos, and lower friction with Sift.
Achieve up to 285% ROI
Increase user acceptance rates up to 99%
Drop time spent on manual review up to 80%
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