AI in Catastrophe Modeling: Embedded in the Science

May 11, 2026 / 4 min read

Artificial intelligence is reshaping how the insurance industry understands and responds to catastrophe risk. Hardly a day goes by without AI making the headlines. For insurers and reinsurers navigating capital and risk management decisions, the noise can be difficult to cut through.

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At Verisk, AI is not the headline. We leverage AI across catastrophe science, modeling workflows, and analytics platforms to improve speed, efficiency, and model quality, while ensuring every output remains grounded in scientific rigor and fully defensible.

Science first. AI second.

The core of Verisk’s catastrophe models remains grounded in physics. Decades of atmospheric science, engineering research, and unique claims data underpin how we represent hazard, damage, and loss. Within this framework, we introduce AI techniques selectively to augment well-established physical and statistical representations.

Take our global modeling framework. It builds upon physics-based global circulation models and incorporates advanced AI algorithms to produce hazard simulations that are both statistically robust and locally detailed. First, AI models correct biases in a physics-based climate model output so that frequencies and intensities of extremes align with observed reality. Second, generative AI techniques introduce fine-scale details that capture the local structure and impact of these extreme events.

Together, these AI techniques allow us to generate globally-correlated hazard simulations with a level of realism and diversity that traditional approaches simply can’t match. The result: the industry’s first-ever collection of globally interconnected atmospheric peril models, with more realistic, more stable representation of loss-causing events. It’s grounded in physics and data, providing the most robust risk analytics we’ve ever produced.

This is what we mean when we say AI is an augmentation tool for science. It allows us to unlock new modeling capabilities that were previously out of reach. AI adds stability, robustness, and confidence to our models and analytics. And just like the need for statistics wasn't diminished with physical approaches, the need for physics isn't diminished by AI. Physics is a prerequisite for AI outputs to be explainable to internal stakeholders, defensible to regulators, and consistent across applications.

Where AI is working today

Verisk embeds AI across catastrophe science, modeling workflows, and analytics platforms while preserving scientific rigor and transparency, not as part of a pilot program or proof of concept, but as an integral component of our catastrophe models.

Hazard simulation benefits from AI’s unprecedented ability to learn complex, nonlinear relationships across massive climate data sets. AI also dramatically accelerates simulation of large stochastic event sets. For example, at Verisk, AI is already being used to downscale and augment climate simulations, synthesize storm footprints and rainfall events, and predict wildfire spread dynamics. Together, these capabilities unlock more realistic, detailed, and diverse hazard representations compared with traditional, perturbation-based methods.

Vulnerability modeling gets a boost from AI’s ability to ingest multi‑modal data—such as exposure attributes, aerial and satellite imagery, and claims—at the property level within a single model. In our U.S. wildfire model, we use AI to extract information on structural characteristics, defensible space conditions, surrounding vegetation, and applicable building codes, all important drivers of wildfire vulnerability. Once trained, the model can process huge volumes of new data and produce a detailed risk report at the property level. Scaling this analysis manually across large portfolios would otherwise be infeasible, but AI enables it, driving more accurate risk assessment and pricing for insurers and reinsurers.

Loss estimation applies machine learning to support faster, more precise portfolio evaluation and improved responsiveness under complex financial structures.

Across all of it are physics-based guardrails, expert validation, cross-peril consistency checks, and transparent documentation of every assumption. scientific rigor, which has always defined Verisk's models, is the constraint within which AI operates and a necessary condition to ensure AI adds value to the modeling chain.

Helping our partners

AI claims are easy to make in today’s market; the real test is whether those outputs can withstand scrutiny internally, with regulators, and in the decisions that shape capital and risk strategy.

Achieving this requires embedding AI within a strong mathematical and institutional foundation—the kind Verisk has cultivated over decades. We integrate limited and imperfect data with domain expertise spanning multiple scientific and engineering disciplines, along with careful orchestration of model components, to create the conditions for AI to deliver meaningful insights that are explainable across teams and stakeholders.

Ultimately, the role of AI in Verisk’s catastrophe modeling is not to replace expertise but to extend it. The measured application of AI enables our partners to work with greater speed, consistency, and clarity while maintaining the rigorous standards the industry expects of us.

That is the standard we apply to AI: grounded in science, transparent in its assumptions, and aligned with the realities of insurance.

This is the first in a three-part series on AI at Verisk Catastrophe and Risk Solutions. Part two explores how AI is transforming modeling workflows from exposure preparation to event response. Part three looks at how Verisk is helping clients build toward their own AI strategies.

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