AI is disrupting everything — including how we estimate the financial shock of extreme weather events.
For decades, insurers, reinsurers, and other financial institutions have relied on catastrophe (or ‘cat’) models to gauge potential losses from hurricanes, floods, and runaway wildfires. But these computational tools risk being left behind by the rapidly evolving climate — and the sudden shifts in the frequency, intensity, and geography of weather hazards this entails.
Can AI help the cat model industry keep up? And what could AI-enhanced cat models mean for the re/insurance industry writ large? To answer these questions and more, Climate Proofers welcomes Dan Ward — Atmospheric Scientist at Karen Clark & Company (KCC) — to the pod.
Dan is an atmospheric scientist who’s spent nine years at KCC building models for nearly every weather peril imaginable. He explains the shift from the first generation of cat models — statistics-based systems designed for the predictable, parameterizable structure of hurricanes and earthquakes — to a second generation built for the messier, high-frequency perils that increasingly drive annual losses; and the new generation of AI-enhanced models using advanced machine learning techniques to find patterns in intimidatingly large datasets.
Dan also gets into what this shift means for the people relying on these models. He makes the case that faster, more frequent updates could mean less pricing volatility and fewer disruptive shocks between long update cycles, while cautioning that AI approaches could bring explainability and transparency challenges to financial institutions.
It’s an episode that should have something to teach everybody — from hardened cat model veterans to newcomers curious why these tools shape so much of the insurance and financial system.
👉 Read the Karen Clark & Company paper of AI in cat modeling HERE
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Louie Woodall
Editor
