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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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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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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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This is a preview of the Risky Science Podcast Member Edtion.

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• (00:00) - Introduction
• (00:17) - Guest background: From P&C attorney to insurance law professor
• (02:13) - AI in insurance today: back-office efficiency vs. underwriting and claims
• (10:06) - Is AI "locked and loaded" for underwriters and claims departments?
• (12:24) - The 50-state regulatory problem and its compounding complexity
• (22:05) - Catastrophe modeling and AI in property underwriting
• (30:19) - Why disclosure usually forestalls regulation rather than protecting consumers
• (38:40) - Schwarcz's proposed fix for shadow insurance
• (43:40) - "Obamacare for Homeowners Insurance": the case for insurance exchanges
• (48:56) - Five-year outlook: where is the insurance industry headed?

In this episode I’m joined by Dr. Patrick Brown, Head of Climate Analytics Interactive Brokers

 to talk about modeling the models, forecast contracts, and whether prediction markets could become the next tool in the risk-transfer stack for the institutional market.

Over the past decade, cyber risk modeling has matured rapidly. But as cloud concentration deepens, dependencies multiply, and “near miss” events become more frequent, a central question remains unresolved: what does a truly systemic insured cyber loss actually look like—and are markets prepared for it?

In this conversation with Coalion’s Dr. Morgan Hervé-Mignucci,Head of Risk Modeling at Coalition ,the discussion focuses on how cyber models have evolved, where they still fall short, and why many high-profile disruptions generate far less insured loss than the headlines suggest.

Today’s discussion is a direct counterpoint.

My guest is Dave Jones, former California Insurance Commissioner and now director of the Climate Risk Initiative at UC Berkeley Law. His recent article argues that insurance itself has become the clearest early-warning signal of climate risk—describing property insurance as the “canary in the coal mine,” and warning that the canary is already dying.

This conversation is timely because the stress is no longer theoretical. Catastrophe losses are accelerating, insurers are pulling back from high-risk regions, and residual markets are expanding rapidly. Jones argues that neither deregulation nor rate increases will be enough if the underlying drivers of loss continue to intensify.

We’ll examine California and Florida as live case studies, what mitigation and modeling can realistically achieve in the near term, and where the practical limits of insurance may already be coming into view.

In this episode, I’m joined by Roger Pielke Jr., a researcher known for his work on the use—and misuse—of models in risk and policy decisions. Pielke is a polarizing figure in climate research, particularly for his views on how climate change should—and should not—be incorporated into catastrophe models used for annual insurance and reinsurance decisions.

It was a timely conversation, especially as Pielke has recently used his Substack, The Honest Broker, to critique the current state of climate-risk analytics and modeling. Whatever your view of his conclusions, they offer a challenging and well-informed perspective on how risk models are being used today.

This week on the Risky Science Podcast, I’m joined by Dr. Phillip Mulder of the University of Wisconsin, co-author of a newly released research paper examining how these insurance pressures are influencing who buys, who moves, and who can no longer afford to stay.

The research has drawn significant attention, including coverage in The New York Times. Dr. Mulder explains how one of the primary underlying forces in this emerging economic crisis is a series of “reinsurance shocks” — repricing events driven in part by catastrophe model estimates that are reverberating throughout the U.S. economy.

Tim describes weather as a “sleeping giant,”  a market that should be much bigger given how energy, logistics, agriculture, and retail all rely on predictable weather and stable demand

We’ll talk about where demand is growing fastest, how better forecasting and satellite data are transforming hedging strategies, and why Tim sees weather derivatives and insurance as part of the same risk ecosystem — working together to get businesses the protection they need. 

Investable! When Pandemic Risk Meets Speculative Finance

That hasn’t stopped real innovation — especially in weather and physical-risk finance, where the market potential is becoming increasingly difficult to ignore.

In this episode, we speak with Jim Huang, founder of climateHedge, a firm with operations in both the U.S. and Shanghai. With a background in product strategy at the CME Group and a deep passion for opening Asian markets to weather-derivative trading, Jim is on a mission to educate the Chinese market. We discuss his on-the-ground progress so far — and how he sees weather derivatives, insurance, and reinsurance products evolving over the next decade.

Risk Market Briefing: Inside China’s Bid to Industrialize Weather Risk Trading

In this conversation, Dr. Singh explains why AI’s biggest weather-forecasting success have been within the seven-day window—and why pushing beyond that horizon remains so hard. We explore how AI can enhance—not replace—traditional physics-based models.

We also get into the practical side: how finance, insurance, and even energy traders are using AI-driven forecasts, what the rise of AI agents means for accessibility, and why transparency and back-testing are critical to overcome the industry’s skepticism toward “black-box” models.

We’ll explore how models have evolved, where they still fall short, and how issues like climate change and artificial intelligence are reshaping the conversation.

• (00:00) - Introduction
• (02:00) - Katrina as a Market Catalyst
• (06:30) - Investor Confidence Under Fire
• (11:00) - The First True Test of Catastrophe Models
• (16:00) - Politics, Policy, and Deductibles
• (22:30) - The In-House View of Models
• (28:00) - Beyond Peak Perils
• (34:00) - AI and Model Acceleration
• (38:00) - A Biophysics Approach to Complex Systems
• (44:00) - Katrina’s Legacy in Today’s Markets
• (49:00) - Why Katrina Still Shapes Investor Confidence and Risk Transfer Today

We explore why Michael is skeptical about California developing a public wildfire model, despite being part of the strategy group that studied it. We'll dig into how the newly approved private wildfire models are about to transform California's insurance market. And we'll discuss something that's crucial but often overlooked: how community-scale risk mitigation efforts can and should be integrated into these models.

We'll explore why these storms are so much harder to model than hurricanes, what new data sources are filling the gaps in our understanding, and why we're still five to ten years away from having reliable catastrophe models for severe convective storms. 

In this episode, we dive into why academic and private-sector research in catastrophe risk have grown apart—and what it takes to reconnect them. We talk about multi-hazard risk, the power and limits of AI in modeling, the role of surrogate models, and how future disasters—from wildfires to earthquakes—demand new thinking.

Our guest is Dr. Jeremy Sepinsky, Lead Wargame Designer at CNA, a federally funded research and development center that supports national security analysis.

We dive deep into how wargaming differs from traditional combat modeling, how escalation and de-escalation decisions are captured through human dynamics, and what all of this means in the context of current tensions with Iran.

Whether it’s simulating natural disasters, cyber warfare to strategic miscommunication, Dr. Sepinsky explains how wargames help reveal the unpredictable—but actionable—dimensions of risk that quantitative models alone can’t capture.

Risk Market News: Risk, Models and Markets

Whitaker also offers a behind-the-scenes look at Oasis’s technology roadmap, including performance upgrades for large portfolios, growing support for cyber and climate-conditioned models, and new tools tailored for low-resource environments. From global reinsurers to national risk pools, the episode explores the expanding use cases for open modeling and what success looks like for an ecosystem built on collaboration. Whether you're a model developer, regulator, or insurance professional, this episode offers a candid look at the future of catastrophe risk analytics.

In this episode, we kick things off with a conversation about weather and climate modeling with Dr. Daniel Swain, a climate scientist at the University of California Agriculture and Natural Resources and the National Center for Atmospheric Research. Daniel is best known for his work on extreme weather events, climate change communication, and for developing the "weather whiplash" framework to describe rapid shifts between extremes. He’s also the voice behind the widely followed Weather West blog.

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