In recent years I’ve shared my views on the fintech trends shaping travel. This year I’m taking a slightly different approach. Rather than examining the impact of financial technology on travel, I’m focusing on the various ways artificial intelligence (AI) is likely to enhance travel fintech in the year ahead.
There’s a good reason for this. Every now and then, something so influential arrives that it deserves a single focus. In my view, the recent advances in generative AI combined with the wider application of machine learning represent such a moment. Here are the three AI fintech trends I believe will shape travel in 2024:
Super-charging payments orchestration
For any travel company operating across multiple markets, a payments strategy is only likely to be as effective as the orchestration logic that governs these payment flows. With each payment transaction there’s the potential to take correct or incorrect decisions about how it’s handled. For example, if the payment relates to a high value booking and to a route an airline is seeking to grow, while showing a low fraud risk, perhaps it should receive the “priority” treatment. Conversely, if a payment relates to a low value booking and carries a higher fraud risk, it likely needs to be treated in a different way.
There are many ways to optimize how each payment transaction is handled. For example, should we apply a “strong customer authentication” exemption to this payment? Should we route the transaction differently depending on the card issuer’s country? Do we have a high degree of confidence in this transaction so we can skip thorough fraud screening on this occasion?
Payment orchestration platforms make thousands of such decisions on behalf of travel companies every day. Here too, the industry is sitting on a wealth of data that can provide context on the underlying trip, which will greatly enhance payments outcomes when combined with AI. This is a huge opportunity to increase acceptance rates and to better align payments to a travel company’s broader commercial strategy.
Identifying payments fraud more effectively
With payments fraud expected to surpass $362 billion by 2028, according to Juniper Research’s Online Payment Fraud research, developing more effective ways to deal with the issue is key for travel companies.
Today’s fraud screening solutions are effective, but with greater application of machine learning they can play fully to travel’s strengths. The travel industry has vast data resources that can be harnessed to improve the quality of fraud decision making.
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For example, when a travel provider receives an incoming payment it’s possible to see that:
- The person has flown hundreds of times before.
- The passenger’s passport and payment credentials have never been rejected before.
- 95% of this passenger’s travel relates to business and 5% to family trips.
- To understand the channel this person typically books through.
Machine learning makes it possible to harness this trip context to improve risk scores that will help identify more fraud and reduce “false-positives,” when a payment is indeed genuine.
Automating chargeback management
Our own research recently found that travel companies are seeing 30% annual growth in payments disputes as more travelers are choosing chargebacks over refunds. The process of tracking, responding to and contesting a chargeback is largely manual today, but it stands out as a task where generative AI could soon play a role.
A well-trained model could do a great job of analyzing the dispute. Sourcing travel and payments information that relates to the customer and their booking before deciding how the travel company should respond. It could then automate the creation of communications with the customer and the standard documents that must be prepared every time a merchant chooses to contest a chargeback.
When combined with an expert team to refine the approach and manage exceptions, generative AI for automated chargeback response would help travel companies stem revenue leakage by contesting more disputes and reducing administrative costs. It is already clear this technology will transform areas of the travel industry that are transaction rich and have large volumes of structured data, like payments.
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