The Travel Industry’s Costly Blindspot
Grow your sales
Learn how fraud management impacts conversion rates & revenue
Expand your business
See which operational bottlenecks impede travel business growth
Read why machine learning is a fraud prevention game changer
Digital travel sales are booming, with worldwide volume set to surpass $676 billion in 2018. Yet even as travel businesses ride this wave of e-commerce growth, digital payment fraud continues to cost billions in unnecessary losses, preventing these organizations from realizing the full potential. While e-commerce fraud may seem like a problem that’s understood and well managed, the reality is that the outdated solutions currently being used by many in this industry can make the problem worse. Read this report published by Skift to learn how to align your fraud management solution with your most obvious goals: sales & growth.
- Executive summary
- Executive Letter
- E-commerce fraud 101: what is it?
- Outdated fraud tools cost travel businesses big money
- Lost revenue
- Higher internal costs
- Poor customer experience
- Why fighting fraud is so hard for the travel industry
- Purchase timing
- Geographic considerations
- The rise of mobile
- Fighting back: machine learning and the future of the industry
- Machine learning for fighting fraud
- Identifying common patterns
- Machine learning and your bottom line
Digital travel sales are booming, with worldwide volume set to surpass $676 billion in 2018. Yet even as travel businesses ride this wave of e-commerce growth, digital payment fraud continues to cost billions in unnecessary losses, preventing these organizations from realizing the full potential. While e-commerce fraud may seem like a problem that’s understood and well managed, the reality is that the outdated solutions currently being used by many in this industry can make the problem worse. One recent study found that false declines of legitimate purchases cost U.S. e-commerce merchants $2 billion dollars more than the fraud these systems were designed to prevent.
In order to recapture the revenue lost with today’s ineffective fraud strategies, travel companies must take a more sophisticated approach to fraud management. New technology allows travel companies to shift their strategy from one that is reactive, slow, and inflexible to one that proactively analyzes threats and adapts in real-time to new changes in the fraud landscape.
The cost of not adopting these new fraud-management tools is steep. Today’s inefficient fraud-detection tools and strategies can have significant long-term consequences, including increased operational costs, poor customer experiences, and lost revenue. Merchants also need to understand that improper fraud management effectively turns away legitimate customers for no good reason, as many of the most widely used systems are insufficient and too rigid to handle today’s rapidly evolving global purchase habits.
These hidden costs of inefficient fraud detection and management are also compounded by a unique set of challenges specific to the industry. The unending push for low prices in this hyper-competitive market creates tight profit margins, and merchants must balance the risks of fraud against customer demand for seamless and speedy purchases. Options are abundant in the online travel world, and, as a result, customers have little patience when their attempt to purchase is thwarted due to overzealous fraud prevention. What’s more, the purchase timing of travel products, the geographic distribution of buyers, and travel shoppers’ growing comfort with mobile purchases all contribute to immense challenges that complicate the process of e-commerce fraud management.
Even though travel companies will always grapple with e-commerce fraud, a dramatic reduction in lost sales can be achieved when they implement better fraud-management solutions powered by machine learning. This branch of artificial intelligence can provide a more modern, flexible, scalable and adaptable solution to detect and prevent fraud. After gathering massive amounts of historical data on legitimate and fraudulent transactions in travel, machine learning can be applied to find which data points are most important and how they should be weighed – instantly.
The benefits of this type of fraud-prevention strategy powered by machine learning are numerous, including lower operational costs, an easier path to scaling the business, and unlocking greater revenue (and profits). Indeed, the level of sophistication enabled by these new tools and techniques is now considered by many to be the best-in-class solution for overcoming today’s constantly evolving fraud challenges. These benefits of a machine-learning fraud approach are evident when examining the case studies of several OTAs, each of which experienced considerable business benefits as a result of their implementation.