The integration of advanced technologies into the travel insurance markets is producing a dynamic, changing landscape of innovation and ethical complexity. Large language models (LLMs) and artificial intelligence (AI) are reshaping sales and marketing, risk assessment, claims processing, and customer engagement, while simultaneously raising critical questions about ethics, equity, and accountability. Whether this represents creative conflict or dynamic disruption, the market is being redefined, and the question arises: is the travel insurance industry facing up to the emerging ethical dilemmas?

Collaboration between AI and LLMs in travel insurance

Sales and customer service

The interface between consumers and their insurance providers has been significantly enhanced by the automation available. The introduction of highly effective chatbots, backed by LLM-enabled AI, allows shoppers to evaluate their policies, shop for specific benefits, and consider recommendations based on their individual needs, travel plans, and activities. This technology enables a level of personalisation that is beneficial for both the buyer and the seller. And it appears from the research that customer approval of AI-enabled chatbots is increasing.

Enhanced risk assessment and underwriting

AI agents are analysing real-time data from multiple sources, including Internet of Things (IoT) devices (such as smartphones, watches, and rings), satellite imagery, and travel patterns, to evaluate risks. LLMs are learning to process unstructured text and data, like medical records or social media activity, to identify nuanced risk factors. For instance, AI might flag a traveller’s destination as high-risk due to political instability, and an LLM may cross-reference travellers’ medical history to recommend tailored coverage for pre-existing conditions. This synergy enables insurers like Allianz to offer dynamic pricing models that adjust premiums based on real-time factors such as weather events or geopolitical crises.

Automated claims processing

Claims processing is significantly more efficient using AI, which facilitates validation of receipts or flight delay notifications, for example. Meanwhile, LLMs have learned to produce human-like responses to keep customers informed. Bajaj Allianz’s AI chatbot, Insurance Samjho, resolves routine queries, allowing human agents to handle more complicated cases. This techno-human handover reduces processing times from days to hours, with some providers, such as Root Insurance, experimenting with real-time payouts for selected claims.

Personalised policy design

With large troves of data regarding travel history, spending habits, and actual behaviours, AI identifies coverage gaps (e.g. adventure sports exclusions), while LLMs craft personalised policy summaries. Insurance software company Ancileo highlights how AI dynamically adjusts coverage during trips – for example, adding baggage protection if a traveller upgrades to a premium airline seat. Startups like Bima use this approach to offer micro-insurance plans tailored to budget travellers.

Collisions at the intersection of innovation: what could possibly go wrong?

Data dependency versus privacy

AI’s reliance on granular data (e.g. GPS tracking from mobile apps) clashes with privacy regulations like the General Data Protection Regulation (GDPR). While telematics devices improve risk pricing, they create ethical tension over surveillance; insurers might infer sensitive details (such as frequent late-night driving) unrelated to travel risks. LLMs exacerbate this by parsing social media for risk signals, potentially discriminating against travellers who post about extreme sports.

And of course, consumers’ attitudes about data dependency versus privacy vary by region and country. “Global technological advances in travel insurance have great potential, but successdepends heavily on local market conditions,” said Thorkild Lykke, Co-Founder of MedCube. “For example, Denmark embraces digital identity systems like MitID for seamless
access to personal health data, while Germany is still developing such infrastructure. Consumer willingness to share data varies by market and hinges on perceived benefits and trust. Transparency is crucial, even if it means revealing guiding principles to competitors, because, without it, consumers will resist.”

Speed versus accuracy

AI prioritises rapid claims adjudication, but the propensity of LLMs to hallucinate, generating plausible but sometimes wildly incorrect responses, can lead to errors. For example,
an LLM might misinterpret a policy clause and deny a valid claim, while AI systems, trained on biased historical data, could disproportionately flag claims from certain demographics as fraudulent.

It’s important to note that all AI tools are ‘trained’ on the available information and data, so any disproportionate representations in historical and ongoing data are carried forward as biases in the systems. The claims history from your demographic, for example (the historical model for underwriting), may never be scrubbed from or changed in the data set, even though your current context would warrant an exception by a human agent.

Regulatory fragmentation

AI-driven tools operate globally, but compliance with regional regulations (e.g. the European Union’s (EU) AI Act versus US state laws) creates conflict.

AI-driven pricing and underwriting models may unintentionally use proxy variables (such as zip code, vehicle type, or occupation) that correlate with protected characteristics, including race and gender, or socioeconomic status.

Even when these sensitive attributes are not overtly included, the AI can generate outcomes that unfairly penalise certain groups, leading to discriminatory pricing or denial of coverage. Munich Re notes that LLMs trained on generic data struggle with jurisdiction-specific insurance jargon, risking non-compliant policy wording. Meanwhile, blockchain-based systems like Etherisc’s Flight Delay face legal uncertainty in markets requiring human oversight for claims. Lykke added: “Errors occur in both manual and automated processes due to complexity, so AI and LLMs should be seen as tools that support human decision-making, allowing service teams to focus on the human side while technology handles verification.”

Ethical issues amplified by technological integration

Bias in algorithmic decision-making

As mentioned above, AI models trained on historical claims data will inherit biases, e.g. denying coverage to destinations with high LGBTQ+ travel rates due to outdated risk assessments. LLMs compound this by reflecting societal biases in training data: a chatbot might inadvertently use discriminatory language when explaining exclusions for mental health-related cancellations. ERGO’s virtual reality (VR) training simulations aim to mitigate such biases, but policy-generating AI requires continuous auditing. Even proofreading tools can introduce biases!

Transparency deficits

The ‘black box’ nature of AI conflicts with insurers’ need for explainability. When claims are denied, customers receive LLM-generated explanations that obscure AI’s decision logic. This lack of clarity violates principles of informed consent, particularly in jurisdictions like the EU, where the GDPR mandates “meaningful explanations” for automated decisions. And it isn’t just the software that can’t explain itself; the algorithms become so complex, so quickly, that in many cases, no-one can explain. A policyholder’s claim may be denied, or she may be offered a higher premium with no clear explanation. When clarification is requested, the insurer is unable to provide a transparent rationale because the AI model’s logic is impenetrable.

Accountability gaps

Determining liability for errors is challenging when AI and LLMs interact. (Question: What do you get when two black boxes connect? Answer: Nobody really knows.) If an LLM incorrectly advises a traveller that vaccinations are unnecessary for a destination, and the traveller contracts a disease, is the insurer, AI developer, or LLM provider liable? Current frameworks lack clarity, leaving regulators, courts, and customers to grapple with fragmented accountability.
AI-powered claims systems can process large volumes quickly, but errors or misclassifications can result in legitimate claims being denied or delayed without human review. And AI may
flag a genuine claim as fraudulent due to a data anomaly (such as a mismatched document or unusual claim pattern), leading to the claim being denied and forcing the policyholder into an appeals process.

The innocent, fraudsters, and AI

Where AI excels is in pattern recognition and flagging suspicious claims, which has helped to reduce fraud. It can also generate false denials, as discussed above, resulting in innocent policyholders being investigated, as well as false approvals, allowing sophisticated fraud to slip through. A customer with a legitimate but atypical claim may be subjected to a fraud investigation, causing distress and delay, while a fraudster can manipulate inputs to evade AI detection.

Navigating the future: balancing innovation and ethics

Regulatory sandboxes and industry standards

Most insurers recognise the ethical and operational risks. Companies utilising AI have encountered bias-related challenges, and a majority are dissatisfied with the current methods for reporting and addressing these issues, according to a recent survey by the Ethical AI in Insurance Consortium (EAIC).

Initiatives like the United Arab Emirates’ (UAE) AI regulatory sandbox allow insurers to test LLM-driven products in controlled environments. The International Organization for Standardization’s (ISO) AI ethics guidelines (ISO/IEC 42001) provide a blueprint for auditing algorithms for bias, though adoption remains uneven. Collaboration among insurers and groups like the Partnership on AI is critical to standardise fairness metrics for travel-related AI. The historical models
for competitive and proprietary behaviour no longer serve the interests of stakeholders in these markets.

Ethical-by-design frameworks

Leading insurers are embedding ethical guardrails into AI development cycles. For example, Ancileo advocates for “bias bounties”, rewarding researchers who identify flaws in algorithms, while Munich Re emphasises LLM fine-tuning on insurance-specific data sets to reduce hallucinations. Transparent AI platforms, such as CAPE Analytics (recently acquired by Moody’s), disclose risk-assessment criteria to policyholders.

Consumer empowerment

Progressive insurers are experimenting with AI literacy programs. Allianz’s VR Experience educates travellers on how algorithms assess risks, while chatbots like ERGO’s allow users to contest AI decisions via LLM-powered appeals processes. Such measures may help build trust while aligning with emerging regulations that require human oversight of critical decisions. One question that emerges from the concept of ‘consumer empowerment’ is the degree to which consumers can, or want to, be empowered. There is a fundamental asymmetry between an insurance company with vast technological resources, as discussed here, and an average consumer, who may simply want to trust that if her bags go missing, she’s covered.

The collaboration between AI and LLMs in travel insurance offers growing efficiency and personalisation, but it intensifies the ethical risks. Conflicts among data utility and privacy,
speed and accuracy, and innovation and regulation will persist and likely become more complex and challenging as technologies evolve. By adopting ethical-by-design principles, fostering transparency, and collaborating with regulatory bodies, the industry can harness these tools to create fairer, more resilient travel ecosystems, without sacrificing the trust
at the heart of the insurance contract.

For more information about Irving Stackpole, please visit: https://stackpoleassociates.com/health-tourism-resources/