Risky Science Podcast

This is the audio recording of our June 11 panel on the 2026 Atlantic hurricane season — three people who actually price this risk for a living, in conversation for an hour.

The ground they cover: where the cat models miss, how the ILS market is pricing the 2026 season, and how a new prediction-market contract could change what it means to "test" a hurricane model.

The Democratic Republic of Congo is in the middle of its 17th Ebola outbreak since 1976 and the WHO has declared a public health emergency of international concern. There are no approved vaccines. No approved treatments. Hundreds of suspected cases emerged before the outbreak was even confirmed. And the surveillance infrastructure needed to track it is operating in an active conflict zone.

For reinsurers and ILS investors, pandemic risk has always been the peril that's hardest to model, hardest to price, and hardest to transfer. The triggers don't work and the data is noisy.. And the market has largely walked away from the problem since COVID.

My guest today has spent his career working on exactly that problem from the science side. Dr. Ben Swallow is a lecturer in statistics at the University of St. Andrews, where he works at the intersection of Bayesian inference, uncertainty quantification, and epidemic modeling. He co-led the uncertainty quantification effort for the Scottish government during COVID. He's worked on Ebola outbreak analysis in West Africa. And he's currently part of the Isaac Newton Institute's program on pandemic preparedness.

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California’s homeowners insurance market, backed by privae capital, is still in retreat. The publicly-backed FAIR Plan is financial buckling. And somewhere in the gap between what the cat models capture and what's actually happening on the ground at the property level, there's a mispricing problem that nobody has fully solved yet.

In this episode of The Risky Science Podcast that wraps up my series of conversations from ClimateTech Connect in Washington this past April, I talk with Brian Bastian, Head of Product at Green Shield Risk Solutions — the analytics and MGA operation that's betting mitigation-first underwriting is the answer the admitted market can't quite get to yet. 

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Ben Fidlow is a Fellow of the Casualty Actuarial Society and leads analytics and risk advisory at Willis. In this episode, he explains how brokerage modeling differs fundamentally from carrier or vendor modeling — it's about what risk means to a specific client, not an aggregate book. He walks through Willis's expanded partnership with Moody's RMS, which lets his team layer their own climate extrapolations on top of current-day model outputs while retaining the ability to explain every step to clients. He also shares where he thinks AI is creating its most immediate value (converting unstructured client data into structured formats at scale), why federal data erosion is a real near-term problem, and what it would take for a direct capital market risk marketplace to eventually disintermediate both insurers and brokers.

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In this episode of Risky Science, recorded at ClimateTech Connect in April, IBHS CEO Roy Wright breaks down why catastrophe models were never designed to price individual risk, why mitigation only works at the neighborhood level, and why insurance markets start to fail when price signals drift away from underlying risk.

The Eaton and Palisades fires are now the most expensive wildfire disaster in U.S. history — and what's happening in Los Angeles right now is a real-time stress test of the entire insurance value chain. From how models priced the risk, to how policies were written and sold, to how claims are being managed on the ground.

Joy Chen is a former deputy mayor of Los Angeles with a finance background, and she runs the Every Fire Survivors Network — 10,000-plus Eaton and Palisades survivors. Her group has spent the last year and a half documenting delays, denials, and underpayments among insured survivors. Among the statistics they point to: a $300,000 median gap between expected insurance payouts and actual rebuilding costs, and a recovery pace slower than any previous California wildfire on record — including the Camp Fire.

The question is whether these are simply the normal costs and challenges of a large catastrophe, or market signals about model adequacy — and what happens to market confidence, and ultimately to capacity, when the system fails at scale.

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This episode is part of a series of live conversations recorded at Climate Tech Connect 2026 in Washington, D.C.

Anil Vasagiri, Head of Risk Data Solutions at Swiss Re is a rare combination of technical depth and commercial perspective to catastrophe risk — he came up through Verisk, where he held senior roles in product management and data strategy, before joining Swiss Re in 2020. Since then, he's led the development of some of the industry's most sophisticated tools for understanding physical risk at the location level, including Swiss Re's acquisition of flood modeling firm Fathom.

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The LA wildfires burned more than a hundred thousand acres. They destroyed thousands of homes. And while they were still burning, people were placing bets on them.

Not insurers. Not reinsurers. Not catastrophe modelers running exceedance probability curves. Anybody with a crypto wallet and an opinion.

That's the world of prediction markets — platforms like Polymarket and Kalshi, where you can trade event contracts on everything from Fed rate decisions to wildfire containment timelines. The industry calls it speculative finance. Critics call it arson betting.

My guest today has been thinking about this longer than most. Jamie Pietruska is a historian at Rutgers University whose work traces the long arc of weather gambling — from illegal temperature pools in American cities a century ago to the prediction market dashboards on your phone right now. Her argument is that what looks new is older than we think, and what looks like progress may be a step backward.

Dr. Peitruska's Aeon article

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We sit down with Dr. Sarah Kapnick at Climate Tech Connect in Washington, D.C. in a conversation covers the time-horizon problem at the heart of climate finance, what the PG&E bankruptcy revealed about the gap between credit models and physical risk, and where AI-generated climate insight ends and hallucination begins.
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This week  I speak with Dr. Christiane Baumeister, a professor at the University of Notre Dame. Her research focuses on global oil market dynamics — disentangling the supply and demand forces that drive prices and developing forecasting models that are designed to perform precisely when markets are most volatile.