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.