UK geocoding and property intelligence specialist Addresscloud has adopted a high-resolution pan-European wildfire risk model developed by artificial intelligence solutions provider Skyblu.
Skyblu’s new high-resolution model provides risk scores across Europe at 20 meter resolution, providing greater granularity than traditional datasets.
The move comes as insurers prepare for another summer with high wildfire risk across Europe, with fires more than doubling in area in recent years compared with the early 2000s, putting pressure on insurers to improve risk selection and pricing.
The model was built using satellite observations from 2001 to 2023 and supplemented with climate and vegetation data to provide forward-looking wildfire probability insights from 2026 to 2030.
It covers territories across Europe and is designed to help insurers identify emerging wildfire risks in areas not historically associated with fires.
The model has been validated against real-world conditions, achieving strong predictive performance (AUC ~0.9) and incorporating key risk drivers including historical fire activity, weather patterns and vegetation types.
The dataset, available through the Addresscloud platform, has been used to support a European portfolio of insurers and MGAs, including those operating in France, Spain and Poland.
The pair said the partnership reflects the growing demand for high-resolution climate intelligence in underwriting, with the companies already supporting wind and hail risk modeling in select markets such as France and Spain, with plans to roll out across wider Europe.
Mark Varley, CEO of Addresscloud, commented: “Wildfire risk in Europe is evolving rapidly and insurers must adapt to a more dynamic and complex risk landscape. Access to precise, forward-looking data is becoming critical for underwriting and portfolio management. By combining Skyblu’s modeling expertise with our platform, we enable insurers to assess risk at a more granular level and make more informed decisions.”
Skyblu CEO Kinga Kita-Wojciechowska added: “We built this model with real underwriting use cases in mind, focusing on accuracy and usability. As climate patterns change, insurers need to understand risk at a more local level. High-resolution data allows them to identify emerging hotspots, manage risk more effectively, and respond to changing conditions with greater confidence.”
