Risky Science Podcast

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.Subscribe to Risk Market New

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.Subscribe to Risk Market News

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.Subscribe to Risk Market News

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 articleSubscribe to Risk Market News

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.Subscribe to Risk Market News

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. 

On today’s episode of the Risky Science Podcast, we’re stepping outside the usual finance lens and into a conversation that will push many of your assumptions about how risk, capital, and human health actually interact. Dr. Susan Erikson: medical anthropologist and author of Investable!, brings a perspective that most risk and financial professionals rarely engage with, but absolutely need to hear. Her work on pandemic bonds and the financialization of global health doesn’t just critique the structures we use; it forces us to rethink what “risk transfer” can and can’t solve when the underlying asset is human wellbeing.Investable! When Pandemic Risk Meets Speculative Finance

China was once seen as a vast, untapped frontier for global risk finance — drawing interest from New York to London. Yet a combination of geopolitical headwinds and trade tensions has cooled expansion plans for many executives hoping to grow their Asian footprint.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

This week I’m joined by Meyer Shields of KBW, one of the most followed insurance analysts on Wall Street. We get into what’s driving that split, how AI and modeling are — or aren’t — starting to show up in real financial performance, and whether today’s property catastrophe discipline is a genuine structural reset or just another hard market waiting to unwind.

This week’s guest is Dr. Hansi Singh, an Earth system scientist who’s worked at the U.S. Department of Energy, taught in academia, and now leads a startup called Plannette.AI, which blends physics and AI to deliver long-range weather forecasts for industries including finance and insurance.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.

Dr. Paul Wilson, Head of Catastrophe and Climate Research at Twelve Securis, a leading insurance-linked securities asset manager, sits down for a live recording of the Risky Science Podcast.

We sit down with Dr. Seth Baum, Executive Director of the Global Catastrophic Risk Institute and research affiliate at Cambridge University’s Centre for the Study of Existential Risk. We explore how societies understand and prepare for global-scale threats—from climate change and pandemics to nuclear conflict and artificial intelligence. Dr. Baum explains why uncertainty is the defining feature of catastrophic risk, why markets struggle to price the unthinkable, and why collective action and governance are essential to tackling the crises that no private market can solve.

Join the Risky Science Podcast for a live discussion with Dr. Paul Wilson, Tuesday, September 23, 11 a.m. ET.Register Here

We speak with Dirk Schmelzer, Partner at Plenum Investments in Zurich. Dirk has spent more than 15 years managing catastrophe bond and insurance-linked securities funds, and he brings a practitioner’s perspective on how catastrophe models are actually used in portfolio management and investment decisions.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.

This week we speak with Karen Clark, founder of Karen Clark & Company, about the evolution of catastrophe modeling and the shift toward higher-frequency, climate-driven events.

John Seo, founder of Fermat Capital, about the lessons of Katrina for catastrophe bonds and models 20 years later. (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

Less than a year after the devastating Los Angeles fires, I’m joined by Michael Wara from Stanford University.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 talk with Dr. Victor Gensini, a professor at Northern Illinois University and one of the leading experts on severe convective storms. Dr. Gensini works with the Insurance Information Institute and has just launched a new center for convective storm research, bringing together academic research and industry needs to tackle this modeling challenge.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 talk with Dr. Tina Dura, a coastal hazard researcher at Virginia Tech, as she unpacks a threat that most risk models still underestimate: Sudden land subsidence from a long expected Cascadia subduction zone earthquake.

We're joined by Michiel W. Ingels, lead author of research that takes stock of the state of climate risk insurance modeling and maps out where it needs to go next.

On this episode of the Risky Science Podcast, we talk with Cory Isaacson, CEO of ReThought Insurance — a longtime tech and insurance executive who’s spent years building new models aimed at making flood risk more accurate, more transparent, and more insurable.

In this episode we speak with Dr. Neil Ferguson, a leading voice in infectious disease modeling and Director of the Jameel Institute at Imperial College London. We talk about how disease models are built, how they’ve evolved over the last two decades, and what happens when they move from academic research into policy, politics, and even the private sector.

We're joined by Dr. Paolo Bocchini, Professor of Civil and Environmental Engineering at Lehigh University, a leading researcher  in catastrophe modeling and infrastructure resilience. Dr. Bocchini is the director of Lehigh’s Center for Catastrophe Modeling and Resilience, and he’s spearheading a new collaboration with Rice University—the Consortium for Enhanced Resilience and Catastrophe Modeling.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.

In this episode of the podcast, we are shifting from modeling physical risks to exploring how wargaming and combat modeling is being used to understand the growing threat of geopolitical conflict.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

In this episode of the Risky Science Podcast, Dickie Whitaker, CEO of the Oasis Loss Modelling Framework, discusses how open-source catastrophe modeling is transforming the risk ecosystem. Whitaker shares the origin story of Oasis and how the platform is working to lower barriers to entry, spur innovation, and increase transparency in a space long dominated by proprietary models. The conversation covers how Oasis has evolved over the past decade, recent milestones in its adoption by major players like Moody’s and Verisk, and the importance of model evaluation frameworks in ensuring trust and credibility—especially for regulators, reinsurers, and developing countries.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.

As insurers seek higher yields in a persistent high-rate environment, private credit has become an increasingly significant part of portfolio strategies. But with that growth comes a new set of questions—about liquidity, transparency, and the long-term implications for credit quality. Carmi Margalit shares S&P’s analytical framework for assessing these risks, highlighting what insurers, regulators, and investors should be watching.

After a long hiatus, we’re back—launching a new series of conversations with finance professionals, academic researchers, and technology leaders focused on a rapidly growing and often misunderstood area of risk: modeling.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.Learn more about Risk Market News