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It’s every vacation rental host’s worst nightmare: you show up to your property after a guest has left and find a trashed home, broken furniture, and missing valuables. An attempt to track down your visitor turns up even more disturbing revelations: not only did they pay with a stolen credit card, but they were using a stolen identity. Even if you’ll be reimbursed through a host guarantee, it’s a major hassle that no one wants to suffer through.  

Happily, scenarios like the hypothetical one described above are extremely rare. And there’s good reason for that – accommodation listing sites and other travel marketplaces are doubling down on security; working hard to keep their good users safe and malicious users off the platform. After all, the sharing economy is built on trust. If terrible experiences were common, neither hosts nor guests would want to use the service.

Travel Companies Fight Many Types of Fraud

Data breaches (like recent incidents at Kimpton Hotels and Restaurants and Omni Hotels) aren’t the only high-tech criminality faced by the travel industry. Unfortunately, large-scale breaches – along with other cybercrimes like phishing – have created a thriving underground market for personal and financial data which provides continual fodder for fraudsters to try their hand at credit card theft, identity theft, account takeover, and scams.

While these types of crimes aren’t unique to travel companies, property listing sites, like Airbnb and HomeAway, have unique challenges when keeping their platforms safe. Unlike hotels, which primarily deal with individual fraudsters using stolen credit cards, travel marketplaces and listing sites face the issue of bad users who may be visible to the entire community. Just a few bad apples on the platform can spoil the experience for all other users.

One scenario that’s generated complaints to the Federal Trade Commission is when a fake host lists a phony property, using images scraped from other sites, to lure in unsuspecting guests. Once a traveler shows interest, the “host” tries to convince them to correspond using a personal email address, and to pay using wire transfer – which is effectively like sending cash through the mail for a rental that doesn’t exist.

If guests run across phony listings or a scammer, or a host has a bad experience with a fraudulent guest, they lose trust in the site. The challenges are compounded when there are two parties on a platform – the host and the renter – since fraud can come from either side, as exemplified by the hypothetical situation above.

A Behind-the-scenes Safety Net

So, how do travel companies keep bad behavior off their sites? One smart technology that’s been increasingly successful in identifying fraudsters and scammers proactively is machine learning. Machine learning is a type of technology that identifies patterns in data, and uses these learnings to predict future outcomes. Netflix has one of the most well-known applications of machine learning – it recommends movies and TV shows you might like, based on what you’ve watched and rated in the past. An email spam filter is another commonly recognized example.

When it comes to spotting travel industry fraud, machine learning can ingest all different kinds of data – IP addresses, browser, clicks, email addresses, data on where the listing is located, and more – from multiple sources, in real time, and use that to immediately determine whether a user is likely to be legitimate or a scammer. In this way, travel sites can block bad users from their platforms before they can do harm.

But what does this mean for hosts and guests who rely on these platforms? In short, it means you benefit from an additional layer of security running in the background, at all times. Sift Science, is one company that provides anti-fraud solutions for Pillow and other travel companies like Airbnb, HotelTonight, and Travelmob on everything from ticket fraud and credit card fraud to phony property listings. Companies that use third-party or in-house anti-fraud solutions leveraging sophisticated technology like machine learning are better able to protect their users. And for everyone involved – guests, hosts, and travel businesses – that means peace of mind.

Written by: Sift Science

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