A version of this article was originally posted on tnooz entitled “Travel firms battling on the front line of fraud.

Modern travel is exceptional. Forget the marvels of the actual travel experience – airplanes with wifi, bullet trains that average 200+ mph, boutique hotels that cater to every whim of weary travelers. Perhaps more incredible is that, with the click of a mouse or the swipe of a finger, users can book flights, reserve hotel rooms, and make travel arrangements effortlessly and instantly. The convenience and near-anonymity of travel sites and apps eliminate the face-to-face elements of holding on the phone, standing in lines, and coordinating with physical travel agents. Just a credit card can give customers access to world of wonderful experiences.


But this convenience and access come with a price for travel companies. The relatively low cost, lack of physical shipping address, and immediate acceptance or denial of payment on travel sites and apps make these businesses a target for credit card and account fraud. When combined with the rise in popularity of on-demand businesses, companies like Airbnb, TravelMob, and HotelTonight are finding that they must learn to predict fraud before it strikes; combatting fraud after the fact is too late. Sift Science helps these and other travel booking businesses to identify and prevent bad users, saving time and money for our customers.

The Challenge

Flight or train reservations, car rentals, last-minute hotel bookings: what these travel services share is a need for competitive prices and quick acquisition of open reservations.The real-time need to book and confirm reservations means these businesses don’t have the luxury of manual review time.

Vacation rental marketplaces face an additional fraud threat. Even more damaging than losing a sale to a chargeback is losing a good customer. When travel marketplaces are plagued with fake listings, good users have bad experiences. Every time a good customer pays for a vacation rental that doesn’t match the photos or reviews or – even worse – doesn’t exist at all, that customer loses trust in the travel site. Every bad experience compounds, as lost future travel and requests for refund add up.

The Situation

When a bad user books a reservation with a stolen credit card, diligent travel sites get to work immediately to acquire that hotel room or whatever travel service requested. Many businesses offer last-minute or steeply discounted prices on luxurious travel and accommodations. If Freddy Fraudster takes a stolen credit card number and uses it to book a first-class flight tonight and a hotel for tomorrow, by the time the travel businesses learn through a chargeback that fraud occurred, the travel will already be over. The site has already paid for the room or service, so not only do they lose the profits of a sale, but they’re also out the cost of the booking, as well as the associated chargeback fee or penalty that the issuing bank may levy.

Reducing friction for customers also makes it easier for fraudsters to take advantage of the system. Perhaps a vacation rental site offers the option of anonymous or guest checkout, where only minimal information is collected from the user. The goal of this option is to reduce the number of hoops that good users need to jump through. Don’t want to log in? Fine! Prefer to not share your phone number? Understandable. But these steps often help to set the customers apart from the fraudsters that use travel sites and apps to test stolen credit card numbers or book with stolen payment information.

How To Stay Ahead of Fraudsters

Staying ahead of fraudsters is the only way for travel businesses to protect themselves and their good customers. No matter the speciality –whether flights, ride-sharing, hotel bookings, or user marketplace – today’s travel sites need the insight of large-scale machine learning.

The Sift Science global machine learning model is a solution that allows fraud analysts to benefit from fraud instances and patterns observed and reported worldwide. The real-time element ensures that travel sites, who rely on instantaneous reservations to secure bookings, have the most up-to-date data on known fraudulent attributes and connections to bad users. Visualizing these connections allows for proactive fraud fighting, rather than waiting for the chargeback or complaint to arrive. Our easy API integration allows for instantaneous feedback into the Sift Science system, ensuring that the most up-to-date information is always at your fingertips. The moment that a user in our network is flagged as fraudulent, all analysts are in the know.


You may find that certain habits repeat among fraudsters. Use those indicators to train your unique model to catch the fraud specific to your site, app, or marketplace.

Even more powerful, however, is Sift’s customizability. Every one of our customers is unique and their data offers highly specialized insights into the behaviors of the good and bad users on their sites. Sift Science can be automatically tailored to any fraud detection need with real-time feedback. Notice that hotel reservations from Canada are less likely to be fraud? Assign a high Sift Score threshold for review. Seen that last-minute rental car bookings tend to be fraud? Send Sift the local booking hour, and we can use that data to find fraud for you even more accurately.

Our travel industry users know the power of large-scale machine learning. Powered by the data in Sift Science’s network, reservation marketplaces and booking apps can stop the bad guys before our customers lose the sale. Check out our travel integration guide to see how our Solutions Engineers recommend customizing your Sift Science model, and start stopping fraud today. As always, your first month with Sift is free, so sign up today.

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Jason Tan

Jason Tan is the co-founder and CEO of Sift. Fueled by a passion for building great products and amazing teams, he's also held leadership and engineering roles at BuzzLabs, Optify, and Zillow.