As the travel insurance industry evolves to meet increasingly complex consumer expectations, providers are turning to artificial intelligence (AI), machine learning, and real-time analytics to drive innovation across policy personalisation, risk assessment, customer engagement, and internal efficiency. These technologies are reshaping not only how products are delivered, but how risk is managed and explained in real time. Leading voices in the sector offer a look into how far the industry has come – and how far it still has left to go.

Technology to personalise policy

In an age of tailored experiences, personalisation has become a priority for travel insurers. Leveraging advanced data tools, Squaremouth is refining its approach to bespoke coverage. “We use data analytics, AI, and machine learning to tailor travel insurance policies to individual needs by analysing data like travel itineraries and claims history,” explained Ned Tadic, Manager of Public Relations at Squaremouth.

Daniel Durazo, Director of External Communications at Allianz Partners USA, described a similar focus on tailoring coverage through technology. He highlighted the company’s use of a quotation and optimisation platform that integrates machine learning and rules-based segmentation. “It integrates with major travel suppliers – such as airlines, online travel agencies, and hotels – and operates across digital channels and customer touch points,” he said. The goal, he added, is to use the data already available at different stages of a customer’s journey to tailor policy offerings based on the specifics of that trip.

This approach to intelligent product delivery allows insurers to offer coverage that aligns more closely with travellers’ actual risks and behaviour, moving away from generic one-size-fits-all policies.

The value of real-time risk assessments

Real-time risk evaluation is rapidly becoming one of the most transformative tools in the insurer’s arsenal. From natural disasters to political unrest, rapidly shifting global events can influence a traveller’s exposure, and insurers are seeking to respond dynamically.

“Location-based alerts allow us to offer additional protection, ensuring fair pricing based on current conditions,” said Tadic, referring to how Squaremouth integrates real-time environmental and geopolitical data into its coverage logic.

There’s ample evidence that large language learning models (LLMs) can produce algorithms which more accurately predict risk

Irving Stackpole, President of Stackpole & Associates, is seeing similar trends. “There’s ample evidence that large language learning models (LLMs) can produce algorithms which more accurately predict risk,” he said. But he also pointed out that the application of these tools appears uneven. “Larger providers probably offer a less annoying set of gauntlet questions because they have larger data sets on which to train their chatbots,” he observed, noting a disparity in customer experience that reflects back-end investment and data capabilities.

Stackpole also warned of AI’s limits in terms of consumer trust: “There are situations requiring sensitivity, empathy or subtle discernment where an algorithm is not preferred, regardless of whether it produces ‘more accurate’ pricing or risk assessment.”

From a UK market perspective, Greg Lawson, Head of Travel Insurance at Collinson, noted that the economic constraints facing retail travel insurers limit the applicability of advanced tools like AI-powered parametrics. “The retail UK travel insurance market is low margin and price-sensitive,” he said, “and these benefits are unlikely to justify big investments in this technology.” However, he acknowledged that standalone real-time solutions are gaining traction – especially when embedded in loyalty programmes or card-linked benefits.

Ultimately, Lawson argued, medical-related cancellation and emergency treatment remain the biggest drivers of claims. “Without access to customer medical records and behaviours,” he explained, “there is not as much ability to apply real-time risk assessment.”

Improving understanding of policy coverage

Despite technological advances, policy clarity remains a major hurdle in travel insurance. “Nobody reads the small print!” Stackpole exclaimed. “Not only is this a truism, but there is very good evidence that insurance consumers rely upon crude summaries and do not read coverage details.”

To combat this, Squaremouth has been working on simplifying the language in policies and deploying AI-driven tools for interactive support. “We try to simplify language, use visual aids, and integrate AI whenever possible,” said Tadic. “These tools help travellers easily understand their coverage and avoid missing key details.”

Stackpole sees particular promise in natural language processing (NLP) for making complex policies digestible. “NLP can succinctly summarise complex policy documents, highlight key coverage, and point out gaps or important exclusions,” he said. He believes embedding such tools at the point of sale could revolutionise policy engagement.

Without access to customer medical records and behaviours, there’s not as much ability to apply real-time risk assessment

Durazo emphasised Allianz Partners’ efforts to encourage customers to read their policies carefully. “We provide every customer with a 15-day period to review their policy,” he said, “and our customer experience surveys find that customers who are familiar with their coverage and benefits are more satisfied with their purchase.”

Still, Lawson was cautious: “Despite all the regulatory pressure and availability of customer documentation, many customers do not study the 40-plus pages of a policy wording,” he said. Even simplified insurance product information documents (IPIDs) often go unread. But he sees potential in emerging tech to change that: “There is a great opportunity to use AI to support traveller engagement, whether through complex search agents answering claims coverage questions or wallet passes driving interaction close to the point of departure.”

AI-tailored policies to meet individual customer needs

Can AI build a travel insurance policy unique to every traveller? Stackpole is confident: “AI can incorporate a limitless number of variables in developing one policy for one traveller.” He believes the most powerful aspect of LLMs is their capacity for pattern recognition, which enables algorithms to optimise policies over time.

But the direction of optimisation is critical. “If the instruction is to optimise profit, the algorithm will eventually produce policies that favour profit – potentially at the cost of customer satisfaction,” he warned.

He envisions AI evolving to the point of real-time, on-trip policy customisation. “Policies can be customised before purchase and even adapted during a trip,” he said. “In the optimal use case, predictive analytics can enable frictionless, real-time adjustments, ensuring protection and avoiding excess coverage.”

Lawson offered a measured view. “The challenge is how much a travel insurance policy can be personalised, given the primary risk factors are duration, destination, health and age,” he said. Still, he acknowledged that data partnerships – like those between insurers and airlines – can inform smarter policy add-ons, such as sports equipment cover.

Durazo pointed to Allianz Partners’ Fusion CORE as an example of how personalisation is being delivered at scale. “By delivering a product that considers the specifics of a trip as well as the needs of the traveller, customers are happier, better served, and their travel investment is better protected,” he said.

Improving workplace efficiency through AI

Internally, AI is proving just as transformative. “We prioritise AI projects that increase customer loyalty and decrease touchpoint friction,” said Durazo. Allianz Partners is using AI to forecast call volumes, optimise staffing, and triage claims documentation. “We’re testing AI to help our customer service team identify nuances within thousands of state-by-state policies,” he added. This allows agents to better support customers while navigating a complex  regulatory landscape.

Despite all the regulatory pressure and availability of customer documentation, many customers do not study the 40-plus pages of a policy wording

The real power of AI, it seems, lies not just in reshaping the product – but in transforming every link in the value chain, from pricing and underwriting to claims, customer service and compliance.

As the industry stands on the edge of a digitally enhanced era, the imperative is clear: to use technology not merely to improve profit margins, but to deliver meaningful, accessible protection. AI, machine learning and real-time analytics offer exciting opportunities – but only if applied transparently, ethically, and with the traveller’s needs at heart.