Insuring Against Extreme Heat Navigating Risks in a Warming World 2025

Property-level precision data empower insurers, homeowners and communities to adapt to climate risks. For example, Guidewire’s HazardHub uses geospatial data, AI, machine learning and aerial imagery to generate resilience scores, considering factors such as wildfire risk, fire protection, wind exposure, proximity to fire stations and hydrants, and property details like building size and construction type. Guidewire’s analysis of 91,800 home inspections in California reveals that of the homes that implemented all 13 key mitigation measures – such as home hardening, zoning reforms, wildfire-informed development and external buffers – only 26% sustained any damage during a wildfire. However, while individual home hardening plays a crucial role, significant risk reduction, particularly for conflagration risks, requires neighbourhood- or community-wide adaptation measures. Enhanced risk assessment allows insurers to collaborate more effectively with homeowners, businesses and communities to mitigate risks in high-hazard areas. This helps narrow the protection gap, attracts more risk capital to the market and bolsters community resilience.for accurately reflecting real-world dynamics. Although it’s still uncertain exactly how much these advancements will improve extreme heat risk modelling, they appear valuable for enhancing predictive capabilities and resilience planning. Advances in technology and richer data sets are enabling insurers to develop more sophisticated climate risk models, improving their understanding of exposures and enhancing their ability to effectively underwrite climate risks. Improved climate risk data enables the development of forward-looking risk models, reducing reliance on outdated historical data. Analysing yesterday’s data is ineffective in today’s fast-changing climate risk landscape. The insurance industry is making increasing use of machine learning tools to comb through large weather datasets and identify complex climate system relationships. It can use these tools to strengthen insurance company perpetration, alerting and response to weather events in ways that can encourage loss mitigation and reduce business interruption. One example is Sentrisk – an AI-powered platform that allows companies to proactively apply climate risk data to minimize business interruption, optimize risk transfer and reroute supply chains in the event of a natural disaster.44 This tool overlays risk data for geopolitical and natural hazard risks and allows users to see live alerts on disruptions. Developments in sensors and wearables will also be vital for the insurance industry to address the impact of extreme heat. These devices can help carriers collect real-time data to assess, mitigate and assess, mitigate and price, according to heat- related risk.45 These devices are being successfully piloted for exposed workers in high-risk sectors, including construction, agriculture and trucking.46 This is particularly important for the life insurance industry, but in the future, this concept could also apply to physical assets. There are ongoing efforts to develop data infrastructure that provides real- time monitoring of heat stress on physical assets such as roads, bridges, train tracks and other vital infrastructure exposed to heat stress. While improved risk knowledge is critical for understanding and addressing climate risks, it can sometimes lead to unintended consequences. For example, an insurer’s enhanced understanding of climate hazard exposure may result in certain areas being deemed too high-risk to insure, creating challenges for communities and homeowners in accessing coverage. However, market-risk based pricing signals are critical for incentivizing more sustainable and resilient land use planning, building codes and development strategies in the long run.47 CASE STUDY 7 Enhancing household resilience with innovative data solutions Improved climate risk data enables the development of forward-looking risk models, reducing reliance on outdated historical data. Insuring Against Extreme Heat: Navigating Risks in a Warming World 16