Risky Science Podcast

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.

A conversation with Dr. Hui Su, a professor at the Hong Kong University of Science and Technology and one of the leading researchers working at the intersection of satellite data, artificial intelligence, and extreme weather forecasting. Become a member of Risk Market News for access to the full member episode.

We speak with Eric Winsberg: a philosopher of science at Cambridge and the University of South Florida, who has thought hard about what happens when models move from the lab into the world and into policy and markets.

Chris Lafakis and his team did the first analysis to combine Moody's catastrophe modeling infrastructure with a full macroeconomic model. The results are eye opening.This is a preview of the Risky Science Podcast Member Edtion.To get access to the full episode sign up to become a free member of Risk Market News.

Dr. Ben Collier, a professor at the University of Wisconsin-Madison, and his fellow researchers published a recent paper that uses twenty years of Florida data to trace a direct line from cat model revisions to the premiums homeowners actually pay. The finding? A one-dollar increase in modeled expected loss translates to roughly five dollars in higher premiums. That multiplier — and what's driving it — is what we're unpacking today.In the episode we dive deep into the findings.The paper: Pricing Climate Risk: Hurricane Models and Home Insurance Over the Last Two DecadesSubscribe to Risk Market News

A conversation with Daniel Schwarcz, professor at the University of Minnesota Law School, where he teaches insurance law, contract law, tort law, and financial regulation and his academic work sits at the intersection of AI governance and insurance regulation. (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 of the Risky Science Podcast we are joined by Danie Retil, co-founder of Exona Labs, a startup building AI risk modeling and quantification tools.

For decades, insurers, reinsurers and energy companies have relied on models, parametrics, and traditional hedges to manage hurricane and weather exposure. But what if markets could continuously price those risks — in real time — and let anyone transfer or hedge them instantly?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.

In this episode of the podcast, we speak with geopolitical risk expert Samuel Wilkin of Willis Towers Watson about why political risk is moving from a background concern to a front-line business problem. Sam breaks down the rise of “gray zone” attacks in the space between war and peace—from covert sabotage to infrastructure disruption—and explains why these threats are so difficult to model and insure. He also argues that the future of political risk management is less about perfect forecasts and more about scenario discipline, exposure mapping, and governance structures that can keep up with a faster, messier geopolitical cycle.

In our first episode of the New Year we are focusing on cyber risk in 2026, a peril that looks increasingly systemic, yet remains poorly understood when it comes to how losses actually materialize.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.

In the last episode of Risky Science, we examined skepticism around climate-conditioned catastrophe models with Roger Pielke Jr.—questioning how much weight long-range climate assumptions should carry in near-term insurance and capital decisions.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.

Register for the January 8 Risky Science Podcast LiveIn 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.

Register here for the January 8 Risky Science Podcast LiveThis 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.

Our guest is Tim Boyce of TP ICAP, who has spent more than 25 years in financial markets, from US-dollar swaps in London to commodities in Singapore, before returning to the UK to build out the firm’s European weather business. 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 demandWe’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.