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.