UL Research Institutes Unveils Roadmap for Advanced Wildfire Risk Intelligence

UL Research Institutes’ Fire Safety Research Institute has released a comprehensive evaluation of wildfire risk models, offering a roadmap for more accurate risk intelligence. For insurers and reinsurers struggling with escalating wildfire losses, the findings provide a framework to assess modeling capabilities, improve underwriting precision, and better understand the vulnerabilities of property and assets in high-risk zones.

What was announced

The report, titled "Wildfire Risk Modeling Evaluation Findings and Community Perspectives," is the culmination of the 2026 Wildfire Risk Modeling Exercise. This initiative represented a first-of-its-kind collaborative evaluation that utilized common scenarios and datasets to test a wide array of wildfire risk models. The exercise brought together approximately 20 organizations, representing a cross-section of academia, government agencies, national laboratories, and private sector entities. The primary objective was to assess how different modeling capabilities can be applied to meet the specific needs of communities, particularly in identifying high-risk areas and prioritizing mitigation investments.

The findings highlight that current wildfire risk intelligence is often fragmented. The report argues that no single model is capable of capturing the full complexity of wildfire risk. Instead, it proposes a roadmap for a new generation of risk intelligence that links complementary models and datasets. This framework covers the entire wildfire risk assessment pathway, starting from initial ignition and landscape fire spread, through to structure exposure, community impacts, and the resulting economic consequences. To achieve this, the report recommends the development of shared core data standards, improved transparency in how models function, and the expanded use of decision-support tools.

For practitioners such as fire service leaders and emergency managers, the report offers a practical framework to determine which modeling tools are most appropriate for specific community needs. When applied effectively, these tools are designed to help identify high-risk areas, prioritize mitigation investments, and strengthen evacuation and preparedness planning. The goal is to facilitate more informed decision-making before, during, and after a wildfire occurs, bridging the gap between theoretical hazard modeling and the practical understanding of which property and assets are most vulnerable.

The companies involved

UL Research Institutes and its Fire Safety Research Institute (FSRI) function as central nodes in the global safety science community. The FSRI specifically focuses on addressing fire challenges through intensive research and evaluation, which in this instance involved coordinating a massive collaborative effort across the public and private sectors. By engaging approximately 20 organizations—including national laboratories, government agencies, academia, and various nonprofits—the institute positions itself as an objective evaluator of the technologies used to predict and mitigate fire-related disasters.

The organization’s work provides the evidence-based frameworks that public safety officials and emergency managers rely on to make high-stakes decisions regarding community risk and resource allocation. As part of the broader UL Research Institutes network, the FSRI contributes to the development of safety standards and scientific insights that inform both public policy and industrial safety practices. This collaborative approach suggests a role that focuses on the scientific standards that underpin the broader safety and insurance industries, rather than the development of proprietary commercial software.

What this means from an insurtech perspective

From an insurance standpoint, the call for an "interoperable modeling ecosystem" and "ensemble modeling" marks a potential shift away from the industry's reliance on siloed, proprietary "black box" catastrophe models. For carriers and MGAs, the emphasis on transparency and explainability is particularly relevant as they face increasing pressure from regulators to justify wildfire-related rate hikes and non-renewals in high-risk jurisdictions. If the industry moves toward the integrated risk intelligence framework suggested, it could lead to more granular underwriting and more accurate pricing of structure-level exposure. Furthermore, the focus on connecting landscape fire spread to economic consequences directly addresses the current disconnect between physical hazard modeling and actual loss ratio predictability, potentially stabilizing capacity in volatile wildfire-prone markets.