Data breaches at major airlines have been in the news a lot lately, highlighting the increasing supply of basic payment data in the black market economy. British Airways, Air Canada, and Cathay Pacific all lost millions of clients’ credit card numbers, email addresses, passport numbers, and more, which will probably be used in attempts to defraud other airlines and travel industry merchants. What hasn’t changed, worryingly, is that many large airlines still rely on basic fraud checks that can easily be bypassed by 21st-century fraudsters and have yet to implement more advanced fraud prevention solutions based on richer data sets not yet compromised by these fraudsters.
Many airlines today rely on legacy infrastructure and anti-fraud solutions based on technology developed in the ‘80s and ‘90s, such as address verification services (AVS) and card verification numbers (CVN). A quick look at the data available on the open web and in dark web marketplaces would quickly reveal to any payments executive that these identifiers are compromised and can be bought cheaply and in bulk by fraudsters.
Get better data
What are the airlines missing? Today, there are many more classes of data that can be used to authenticate transactions. This includes biometric data, behavioural data, and device identity data. Now, you can determine if a person is who they say they are by authenticating their voice, their thumbprint, or their eye scan. If you are trying to minimise friction in your checkout process, you can authenticate a customer by how they interact with your webpage and/or their device – a technology that is becoming increasingly popular, especially with banks.
You can also add the use of device identity data in fraud prevention, which is becoming commonplace enough that some providers of traditional personally identifiable information (PII) now supply device ID data in their solution offerings as well.
There are dozens and dozens of fraud solution vendors that enable merchants to seamlessly incorporate these new data types into their payment flow. About-Fraud.com regularly updates a list of these vendors, filtered by solution type, so merchants should have no trouble locating them. Unlike older fraud prevention tools like AVS, airlines also need not worry about the geographical limits of these solutions. There are at least a couple of solutions active in every major geographic market and all the new data types and the technology they leverage are truly global in nature.
Fully benefiting from automated risk scoring
Airlines should not be late adopters to advanced fraud prevention technology. Their business model leaves them more exposed to fraud than the typical merchant. Currently, airlines use a number of different sales channels, including their websites, online travel agencies, consolidators, and travel agents, but many still only apply one uniform set of anti-fraud rules across these very different channels. Moreover, airline customers come in every shape and form, from locations all over the world. Some customers still plan their trips months in advance, but the entire travel industry is experiencing growing volumes of last-minute purchases by both business travellers and tourists. This makes it very difficult to create a clear rules set that will block the fraudsters without losing a significant number of legitimate purchases.
Unsurprisingly, a recent CyberSource study found that airlines still need to manually review 18% of orders, despite only 12% of manually reviewed bookings ultimately being cancelled. While this represents a significant improvement – over 27% of transactions were manually reviewed in 2014 –, it is still too high. Bringing down the manual review numbers even further would require not just increased automation but smarter automation, ie artificial intelligence solutions fed with enough meaningful data points that they can make decisions not only faster, but also better than the typical fraud analyst.
Data is the lifeblood of fraud prevention
A handful of major platforms have enabled airlines to bring down their manual review rate and adapt to changing and complex fraud trends with automated risk scoring engines that utilise machine learning models to predict transaction risk. But even the most advanced machine learning algorithms won’t solve the problem of ‘garbage in, garbage out’. Put simply, to dramatically reduce fraud and false positive rates these systems need large amounts of data that can be used to distinguish customer identity and risky transactions.
To cut down on revenue lost to inefficient fraud prevention mechanism, airlines need to spend more time and resources on testing the efficacy of new data types for preventing fraud across different sales channels. The big banks are doing it. Apple and Microsoft are doing it. It’s about time the airline started doing this seriously as well.
This editorial was first published in the Web Fraud Prevention, Identity Verification & Authentication Guide 2018-2019. The Guide covers some of the security challenges encountered in the ecommerce and banking, and financial services ecosystems. Moreover, it provides payment and fraud and risk management professionals with a series of insightful perspectives on key aspects, such as fraud management, identity verification, online authentication, and regulation.
About Ronald Praetsch
Ronald Praetsch is Co-Founder and Managing Director of about-fraud.com. He also consults regularly with merchants, payment service providers, and fraud solution vendors. Before founding about-fraud.com, Ronald spent close to a decade in various payments and fraud prevention roles at Sift Science, Fareportal, Booking.com, and Pay.ON, in both Europe and North America.
About About-Fraud.com
About-Fraud.com delivers expert knowledge on technology and trends to a global community of a fraud fighting professionals. Fraud management is super complex, with online businesses struggling to understand and keep pace with evolving trends, technology, best practices and providers. To these businesses About-Fraud.com provides market research and consulting services.
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