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From 15 epsHost
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Recent episodes
Sticky Liabilities, Illiquid Assets and the Private Credit Perception Gap
Jun 24, 2026
29m 05s
2026 Hurricane Season: Risk, Markets, Models
Jun 17, 2026
1h 06m 02s
Ebola, Statistics, and What Pandemic Science Can Teach Markets
May 27, 2026
54m 43s
Wildfire's Garbage-In Problem With Brian Bastian
May 20, 2026
25m 00s
Modeling Every Risk for Every Client with Willis' Ben Fidlow
May 13, 2026
25m 13s
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| Date | Episode | Topics | Guests | Brands | Places | Keywords | Sponsor | Length | |
|---|---|---|---|---|---|---|---|---|---|
| 6/24/26 | ![]() Sticky Liabilities, Illiquid Assets and the Private Credit Perception Gap | 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. | 29m 05s | ||||||
| 6/17/26 | ![]() 2026 Hurricane Season: Risk, Markets, Models | 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. | 1h 06m 02s | ||||||
| 5/27/26 | ![]() Ebola, Statistics, and What Pandemic Science Can Teach Markets✨ | Ebolapandemic science+3 | Dr. Ben Swallow | University of St. AndrewsWHO+2 | Democratic Republic of Congo | Ebolapandemic risk+3 | — | 54m 43s | |
| 5/20/26 | ![]() Wildfire's Garbage-In Problem With Brian Bastian✨ | homeowners insuranceCalifornia+4 | Brian Bastian | Green Shield Risk SolutionsFAIR Plan+1 | California | homeowners insuranceCalifornia+5 | — | 25m 00s | |
| 5/13/26 | ![]() Modeling Every Risk for Every Client with Willis' Ben Fidlow✨ | risk modelinganalytics+4 | Ben Fidlow | WillisCasualty Actuarial Society+1 | — | risk modelinganalytics+5 | — | 25m 13s | |
| 5/6/26 | ![]() The Wrong Model for the Wrong Job With Roy Wright✨ | catastrophe modelsinsurance markets+3 | Roy Wright | IBHS | — | catastrophe modelsinsurance+3 | — | 24m 33s | |
| 4/28/26 | ![]() The LA Fires and the Risk Market Value Chain With Joy Chen✨ | wildfire disasterinsurance value chain+3 | Joy Chen | Every Fire Survivors Network | Los Angeles | LA firesinsurance payouts+3 | — | 42m 48s | |
| 4/22/26 | ![]() How Catastrophe Models Work and Where They Fall Short With Anil Vasagiri✨ | catastrophe modelsrisk management+3 | Anil Vasagiri | Swiss ReVerisk+1 | Washington, D.C. | catastrophe riskrisk data solutions+3 | — | 27m 49s | |
| 4/15/26 | ![]() Why Mixing Catastrophes With Prediction Markets Is More Dangerous Than It Looks With Jamie Pietruska✨ | prediction marketscatastrophe betting+4 | Jamie Pietruska | PolymarketKalshi+2 | LA | prediction marketscatastrophe betting+6 | — | 48m 27s | |
| 4/13/26 | ![]() AI, Models, and the Limits of Climate Assumptions with Sarah Kapnick✨ | climate financeAI in climate+4 | Dr. Sarah Kapnick | Climate Tech ConnectPG&E | Washington, D.C. | climate financeAI+4 | — | 27m 23s | |
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| 4/1/26 | ![]() Can Models Still Work When Everything Changes at Once? With Christiane Baumeister✨ | oil market dynamicsforecasting models+3 | Christiane Baumeister | University of Notre Dame | — | oil pricesmarket dynamics+3 | — | 52m 53s | |
| 3/25/26 | ![]() (Preview) China's Growing Risk Data Moat and the US Brain Drain With Hui Su✨ | Chinarisk data+4 | Dr. Hui Su | Hong Kong University of Science and Technology | ChinaUS | Chinarisk data+4 | — | 5m 37s | |
| 3/18/26 | ![]() Confidence as a Service With Eric Winsberg✨ | philosophy of sciencemodels+3 | Eric Winsberg | CambridgeUniversity of South Florida | — | confidence as a serviceEric Winsberg+4 | — | 1h 00m 50s | |
| 3/11/26 | ![]() (Preview) The $232 Billion Storm No One Is Pricing With Moody's Chris Lafakis✨ | catastrophe modelingmacroeconomic analysis+3 | Chris Lafakis | Moody's | — | catastrophe modelingmacroeconomic model+3 | — | 10m 04s | |
| 3/4/26 | ![]() How Hurricane Risk Really Gets Priced with Dr. Ben Collier✨ | hurricane riskinsurance premiums+3 | Dr. Ben Collier | University of Wisconsin-MadisonRisk Market News | Florida | hurricane riskinsurance premiums+3 | — | 55m 40s | |
| 2/18/26 | ![]() Black Box Problems, Machine Judgment and the Rules Nobody's Written Yet With Daniel Schwarcz✨ | AI governanceinsurance regulation+5 | Daniel Schwarcz | University of Minnesota Law SchoolObamacare | 50-state | insurance lawAI in insurance+5 | — | 53m 00s | |
| 2/11/26 | ![]() AI Risk, Markets and Modeling the Unknown With Daniel Reti✨ | AI riskmarket modeling+3 | Daniel Reti | Exona Labs | — | AIrisk modeling+3 | — | 42m 24s | |
| 2/4/26 | ![]() Prediction Markets, Parametrics and Rethinking Weather Risk With Dr. Partick Brown | 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. | 53m 59s | ||||||
| 1/21/26 | ![]() Greenland, Venezuela and the New Political Risk Model Reality with WTW’s Sam Wilkin | 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. | 46m 48s | ||||||
| 1/14/26 | ![]() Cyber Risk in 2026 and Why Near Misses Matter More Than Losses With Morgan Hervé-Mignucci | 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. | 41m 57s | ||||||
| 12/24/25 | ![]() Climate, Markets and the Limits of Insurability with Dave Jones | 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. | 51m 52s | ||||||
| 12/17/25 | ![]() Climate, Catastrophe Models and the Limits of Prediction with Dr. Roger Pielke Jr. | 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. | 42m 17s | ||||||
| 12/10/25 | ![]() Housing Prices, Climate Signals and Reinsurance Shocks with Dr. Philip Mulder | 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. | 49m 41s | ||||||
| 11/28/25 | ![]() Weather, Risk & Europe’s Energy Upheaval with TP ICAP’s Tim Boyce | 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. | 45m 04s | ||||||
| 11/19/25 | ![]() Pandemics, Models and the Limits of Securitization with Dr. Susan Erikson | 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 | 47m 42s | ||||||
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