As the market for physical risk analytics continues to expand, a new report from the UK’s Financial Conduct Authority’s Climate Financial Risk Forum (CFRF) highlights both the complexity and divergence present in current solutions. The study outlines how differences in modeling choices—such as granularity and validation against real-world data—directly affect the quality and reliability of outputs.
“Although we know that the overall threat level is rising, it is not straightforward to translate this into how physical risks might impact individual firms, including those in the financial sector, or even individual assets. For this, a granular spatial resolution of analysis is needed. The risk profiles for a bank, insurance company, asset owner or asset manager differ — as discussed in many CFRF documents over the years — but all firms share the common challenge of how to assess the physical impacts that they are exposed to, be that in their own operations or in the firms that they lend to, underwrite, insure or invest in.” – CFRF
Moody’s notes that stakeholders must navigate inconsistencies related to definitions, data availability, modeling resolution, and analytic scope—a point emphasized by CFRF. The insurance industry’s decades-long use of third-party models for assessing physical risks offers valuable best practices for model evaluation. These practices can help other sectors develop confidence when investing in forward-looking risk models.
Key factors such as hazard assumptions, vulnerability data quality, spatial detail, and data sources can lead to significant differences between model outcomes. As a result, Moody’s stresses that financial institutions should prioritize robust validation standards when selecting analytics tools.
The insurance sector’s experience since events like Hurricane Andrew in 1992 has led to catastrophe (cat) models becoming central to business strategy and regulatory compliance. These models have helped insurers better price policies according to risk and determine necessary reserves. Over time, cat modeling has become more rigorous by integrating detailed hazard assumptions with real-world event comparisons and refined spatial analysis.
Moody’s suggests three main benefits for organizations adopting physical risk solutions based on insurance industry lessons: specific location-level insights using reliable science; validated models with ongoing improvement processes; and consistent terminology enabling clearer communication across industries.
The need for accurate location-level assessment is underscored by Moody’s approach of incorporating extensive real-world claims data and engineering studies into its foundational models. This allows customers—from lenders evaluating creditworthiness in high-risk regions to investors managing portfolio diversification—to make informed decisions at multiple scales.
Transparency remains crucial as regulatory pressures increase demand for clear reporting on climate-related risks. Decision-makers are encouraged to seek solutions where model inputs—hazard estimates, vulnerability assumptions, spatial details—and update processes are openly documented.
A consistent framework also supports better cross-sector dialogue about mitigation investments. For example, banks may focus on property value loss while insurers consider direct damage costs; nonetheless both benefit from shared metrics when discussing adaptation strategies.
Moody’s emphasizes that despite challenges posed by an evolving analytics marketplace, “climate risk is business risk.” Lessons learned from insurance industry modeling can guide other sectors toward resilient strategies through adoption of validated analytics platforms. The company states its commitment to providing necessary data and expertise as businesses adapt their approaches.
