Risky Science Podcast

his summer's record cyclosporiasis outbreak did something unusual: it took money out of companies that had nothing to do with it. Five food companies cut or constrained guidance over the outbreak. None of them served the contaminated product.Hal King has worked every side of that problem — CDC outbreak investigator, architect of an enterprise food safety management program at a major national chain, and now founder of Active Food Safety, advising the largest foodservice brands in the country. He's the author of the definitive books on food safety management systems, with a new one coming on food safety business leadership.We talk about what actually happens inside a brand in the first hours of an outbreak, the five inputs a real predictive risk model would need, why CDC's confirmed case counts understate the true number by a factor of 83, how insurers could price food safety the way they price slip-and-fall risk today, and why a public health advisory — not the pathogen — decides whether an outbreak becomes a supplier story or a brand story.Recorded live on September 8, 2026.

Karen Clark, founder and CEO of Karen Clark & Company, returns to Risky Science to discuss KCC's new white paper on artificial intelligence in catastrophe models — where AI genuinely improves the science, and where the industry is overstating what it can do.Clark built the first commercial catastrophe model in the 1980s. In this conversation she argues that the biggest step change in the field wasn't AI at all, but the shift from statistical to physical, dynamical models — and that AI's real value is narrower than the hype suggests. It sits almost entirely in the hazard component, sharpening intensity footprints for frequency perils like severe convective storm and winter storm, where high-resolution atmospheric data is abundant. The vulnerability and financial components remain largely untouched.We also get into why she thinks a $100 billion aggregate loss year isn't as significant as the industry treats it, given that a single Category 5 hurricane into Miami would cause over $200 billion on its own. She explains KCC's daily live events process, which has been ingesting 30 gigabytes of atmospheric data and producing hail and tornado footprints every day since 2018, and why she believes that testing regime is what makes the models credible. She makes a pointed claim about Winter Storm Uri: KCC had the 2021 Arctic air outbreak at a 1-in-75-year return period, while she says other modelers didn't have it inside 10,000 years.Recorded ahead of the Monte Carlo Rendez-Vous, the conversation closes on hyperscale data centers — an emerging exposure where the modeling question is less about the buildings than about the data going into the models, and where Clark reframes tornado risk in a way that cuts against how most CEOs are thinking about it.

Private credit didn't just find a new source of capital—it may have changed the role of life insurance itself. Andrew Granato and Pranjal Yadav explain why insurance regulation, guaranty funds and opaque private assets could become increasingly important to financial stability.

For years, the biggest competitive battles in insurance have been between insurers. Underwriting. Pricing. Capital management.But artificial intelligence may be changing the rules.What if the next major competitor doesn't come from another insurance company at all? What if it comes from the technology sector?As AI becomes better at predicting losses, preventing claims, and influencing behavior, the question isn't simply whether insurers will use AI. It's who ultimately owns the data, the customer relationship, and the economics of managing risk.Today's guest is Alex Chan, an economist working as an Assistant Professor at Harvard Business School, whose recent research explores how artificial intelligence could fundamentally change the economics of risk. Subscribe to Risk Market News https://www.riskmarketnews.com/

Listen to the full replay of the latest Risky Science Live "Private Capital, Public Flood: Risky Science Live". The panel examines what a larger private flood market actually requires: the modeling sophistication to price the peril credibly, and the underwriting and capital structures to carry it. Flood has long been the catastrophe risk the private market avoided, deterred by sparse data, correlated losses, and a federal backstop that absorbed the tail. Subscribe to Risk Market News

Jane Kim just finished first in the race to become California's next insurance commissioner. She's a former San Francisco supervisor and worked for Bernie Sanders. And where Allen wants to draw private capital back into the market, Kim wants to build a public one: a nonprofit disaster insurer, funded by premiums the state would claw back and reinvest, plus a single catastrophe model built by California itself.So this is the same problem — an unaffordable, retreating wildfire market — seen through two completely different lenses. Allen's answer is a better-run private system. Kim's answer is a public alternative to it.

Weather derivatives have struggled with liquidity for 25 years. Jim Huang thinks prediction markets can finally fix that — but only if the contracts are built around events people actually watch.In this return conversation with Risk Market News, the former CME product strategist and founder of Climate Hedge explains his pivot to WeatherBook, a weather-focused prediction market platform. Huang breaks down why China consolidated all weather index development into the Guangzhou Futures Exchange — pushing any weather futures launch to late 2027 or beyond — and why that regulatory reset opened a window for an event-driven alternative.Register for Risky Science Live

In this week’s episode we are digging into California's insurance market — and specifically, into the fight over who actually owns wildfire risk. Is it the insurers pulling back from high-risk areas? The utilities whose equipment sparks some of these fires? Or the ratepayers and taxpayers left holding the bag when the bill comes due?To help sort through it, I sat down with California State Senator Ben Allen, who's running for Insurance Commissioner. We got into reinsurance costs, catastrophe models, the FAIR Plan, capital requirements — and where the state's private-market approach diverges from the public alternative his opponent is proposing.

This week we focus on the tragic catastrophe in Venezuela, where powerful earthquakes tore along the San Sebastián fault and devastated Caracas. As a result, buildings collapsed, the airport shut down, and the death toll climbed.And a fragile country recently facing its own political upheaval was left asking how this could happen on a fault everyone knew was dangerous.My guest is Ziggy Lubkowski, earthquake engineer and Arup's global seismic expert. Arup is a global engineering and design consultancy, and Ziggy has spent nearly forty years studying the built environment—why some buildings stand and others fall—from Indonesia to Turkey to California.We talk about what really drives the damage, why early loss estimates swing by billions, and the one earthquake-risk investment that pays back six to one.Subscribe to Risk Market News

A year ago, I sat down with Carmi Margalit, Managing Director and the Life Insurance Sector Lead at Standard & Poor’s Annual Insurance Conference to talk about private credit on insurance balance sheets . So when I caught up with him again this year,I figured it was time for a check-in.And here's the thing that struck me: Carmi says not much has actually changed. The allocations have been growing for years. What's changed is everyone watching — the headlines, the regulators, the questions. So a lot of this conversation is about separating the perception from the mechanics.We get into why the redemption gates you've been reading about are a private-credit-fund story and why this is a liquidity question and not an asset-liability question. We also get into issues like the growing role of offshore reinsurance and what it does to transparency. And what's actually on an analyst's radar for mortality and longevity — GLP-1s included.

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.Subscribe to Risk Market News

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. Subscribe to Risk Market News

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.