Singapore Airport Tests AI for Baggage Security

Singapore’s Changi Airport is reportedly gauging whether artificial intelligence can reduce security check-in times.

The airport is using AI and machine learning at one of its terminals to examine and interpret X-ray images during baggage check-ins, The Straits Times reported Sunday (Nov. 26).

According to the report, airport officials said this cuts down on the time needed to process the images and lowers the likelihood of human error. So far, the AI-powered system has done as well as human security screeners — and in some cases exceeded them — in spotting some of the prohibited items that it has been trained to look for, such as cigarette lighters or sharp objects.

If the tests go well, the airport believes it could reduce check-in times by half, the report said.

The test is happening as consumers are demonstrating an interest in mixing AI with travel, as shown by the increase in digital travel-planning tools helping to create personalized travel itineraries and generative AI chatbots helping people book flights.

In all, 63% of consumers said they are at least slightly interested in AI-enabled travel, according to PYMNTS Intelligence data.

This interest has led travel-sector companies like Airbnb to invest more in AI. Earlier this month, that company announced it had acquired GamePlanner.AI, a 12-person startup, in a deal reportedly valued at just under $200 million.

“Imagine having a personalized travel assistant at your fingertips, capable of understanding your preferences and curating the perfect itinerary — this is the promise of AI as a travel agent, and it is the future that Airbnb, as well as many other businesses, wants to build,” PYMNTS wrote earlier this month.

The use of AI copilots to improve travel offer platforms with a built-in agent outfitted with the ability to comb through large amounts of data quickly and pull out relevant information to design a travel experience unique to each customer.

“These intelligent systems leverage machine learning algorithms to understand user behavior, preferences, and past travel patterns, ensuring that every recommendation aligns with the traveler’s desires,” the report stated.

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