This piece is by Daria Piwowarczyk, Innovation Solutions Architect, NTT DATA Group
Key takeaways:
· The travel insurance landscape is shifting from predictable risk to constant disruption
· Real-time visibility is now critical for faster, more accurate underwriting, claims and customer communication
· AI and satellite data are enabling insurers to anticipate risk, improve resilience and gain a competitive advantage
Global travel is entering a new era of disruption. Geopolitical tensions, airspace closures and climate-driven events like wildfires are no longer occasional shocks; they’re a consistent part of the everyday landscape. Data underpins the scale of this shift, with global insured losses from natural catastrophes reaching $137 billion in 2024, well above the 10-year average of $98 billion.
For insurers, this is not simply a matter of rising claims volumes. It is changing the nature of risk itself and, in turn, how it is priced and managed.
Meanwhile, the protection gap is widening. According to our research, natural disasters alone could drive the global protection gap to $399 billion by 2030. This signals that established models designed for a more stable environment are increasingly under strain. For insurers, remaining competitive requires a more dynamic approach to how risk data is accessed and applied.
The new always-on risk environment
Travel risk is no longer episodic; it’s continuous, interconnected and rarely occurs in isolation. A single geopolitical event can trigger airspace closures, leading to congestion and delays across multiple regions. Climate-related disruptions, from wildfires to severe storms, frequently overlap with infrastructure constraints, creating broader systemic impacts. For insurers, this results not only in higher volumes of claims, but more complex scenarios that can span jurisdictions, products and timeframes.
Risks must therefore be monitored continuously, with an appreciation for how quickly one disruption can amplify another. Static segmentation models struggle to capture this level of interdependence, creating blind spots in both underwriting and claims management.
The necessity of real-time decision-making
Speed and accuracy in decision-making are now critical differentiators in how unforeseen circumstances are handled. When disruption hits, everything happens quickly. Claims spike, clients want answers and insurers need to make fast decisions about exposure, payouts and communication. At the same time, expectations have shifted. Travellers now expect clear, proactive updates when things go wrong, not delays or uncertainty.
Falling behind on decision-making can be costly, both financially and reputationally. Acting on outdated or incomplete data can lead to overpaying claims, missing exposure risks or losing customer trust. Insurers that can respond quickly and confidently are in a much stronger position.
This challenge is further amplified by social media platforms, which rapidly magnify and disseminate negative customer experiences as they happen, while preserving them online in the long term. Reputational damage is no longer contained or short-lived; it can escalate quickly and persist indefinitely. As a result, delays in decision-making are not just operational risks but reputational ones, making speed, transparency and accuracy more critical than ever.
Why legacy models can’t keep up
Traditional risk models are misaligned with today’s volatile environment. Many existing risk models still lean heavily on historical data, assuming a level of stability that no longer exists. With events becoming more frequent and less predictable, historic patterns are no longer a reliable guide, and legacy systems often struggle to support more dynamic approaches.
This reliance on backward-looking data creates a fundamental mismatch between how risk is modelled and how it unfolds. Recent events have made this increasingly clear: Hurricane Otis, for example, intensified far more rapidly than expected, while the Dubai floods highlighted how unprecedented weather patterns can overwhelm infrastructure in ways not captured by historical models. These cases underscore how risk is becoming both more volatile and less predictable, challenging traditional approaches to assessment.
This shift is further evidenced by long-term data trends. According to National Oceanic and Atmospheric Administration (NOAA), billion-dollar natural catastrophe events in the US have increased from an average of just 3.3 per year in the 1980s to around 20 annually today, with 27 recorded in 2024 alone. In such an environment, relying on historical baselines can lead to incomplete or outdated decision-making, resulting in inaccurate pricing, slower response times, and less effective claims handling.
Satellite data as a game changer
A powerful solution to the challenge is embracing satellite data. Advances in earth observation make it possible to track everything from weather systems to infrastructure disruption in near real time. New technologies, such as AI-powered geospatial monitoring, can turn aerial and satellite imagery into property-level risk data for insurers, enabling them to evaluate locations and differentiate between high-risk and low-risk zones within larger problematic areas.
Insurers no longer have to rely on historical patterns or delayed reporting: satellite-enabled intelligence allows them to understand and respond to risk in near real time. However, the real value lies not in the satellite data itself, but in the AI-extracted, actionable insights derived from it. Providing raw imagery alone would place a significant analytical burden on insurers, as interpreting satellite data is complex and time-consuming. By contrast, AI and data analytics transform data into clear, decision-ready insights, allowing insurers to move from reactive to proactive risk management.
For example, underwriting can be strengthened by identifying a resort or hotel located next to a forest with increasing dry season wildfire risk, while portfolio risk management can flag a leisure vessel positioned in a high-risk storm path. This enables more accurate underwriting based on current conditions, supports dynamic policy adjustments as risks evolve, and improves claims processes through objective, time-stamped evidence that accelerates validation and reduces fraud. While satellite data can also be delivered with blockchain-backed certification to ensure integrity, it is the ability to convert it into meaningful intelligence that ultimately drives value.
Crucially, satellite-driven insights bridge the gap between uncertainty and action, helping insurers make better decisions across the whole value chain. To use it to its full potential, insurers should explore partnerships and invest in satellite data capabilities, integrating them into core systems to unlock value and gain a competitive advantage.
However, realising this value depends not just on accessing new data, but on how seamlessly it integrates into existing environments. With many insurers relying on complex legacy systems, adoption must be as low-disruption as possible. By combining system integration expertise with flexible modernisation approaches, satellite-driven insights can be embedded into established workflows or introduced through targeted updates, enabling smoother and faster transformation.
From reactive to intelligence-driven insurance
The future of insurance lies in rapid, intelligence-driven operations. As disruption becomes embedded in the global landscape, the limitations of reactive, historically driven models are becoming increasingly clear. That said, leveraging AI with satellite data provides modern insurers with the speed, accuracy and visibility that they need.
This shift is not simply about adopting new tools; it represents a broader transformation in how insurers operate. Those that embrace flexible data and advanced analytics will be better equipped to anticipate and manage risk proactively, rather than responding after the fact. They will also be able to deliver more responsive and transparent customer experiences, strengthening trust in an increasingly uncertain world.
The message is clear: evolve now or risk falling behind. By combining AI with satellite data and other real-time information, insurers can turn travel volatility into an opportunity – building resilience, improving outcomes and securing long-term competitiveness.
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