Reinsurance's Property-Data Inflection Point: A Conversation with Guy Carpenter's Kevin Van Leer

The Zest: Key Takeaways

• Guy Carpenter's Kevin Van Leer on how property-level data is reshaping reinsurance placement and what's changing for catastrophe modelers.
• A broader shift is underway: carriers are elevating property data quality to an enterprise priority, sharpening the data that powers every decision across the insurance lifecycle — from underwriting and pricing to claims, portfolio management, and reinsurance.
• The bar for AI in insurance has shifted from "interesting" to "purpose-built": systems that are faster, more consistent, and more defensible than the manual processes they replace.

Few people have seen property analytics from as many angles as Kevin Van Leer. After studying atmospheric science at Purdue, he drove the release of the wildfire catastrophe model at RMS, a model whose appetite for granular property inputs ran ahead of what the data side could deliver at the time. He then spent seven years on the property analytics side at CAPE Analytics before joining Guy Carpenter as a Certified Catastrophe Risk Analyst. Today, he sits at the center of one of the most consequential shifts in reinsurance placement in a generation: how brokers, cedents, and reinsurers actually use property-level data to make capital decisions.

In a recent keynote conversation with the ZestyAI team, Kevin shared his view of where this market is going. The themes he covered are worth carrying forward.

Property data quality is now an enterprise imperative 

For decades, the industry has been moving from portfolio averages and class plans to property-level precision — and that shift is now reaching the part of the lifecycle where the most capital is at stake. Property-level data has long delivered value in underwriting and pricing, but the next leap is consistency: carriers building a single, trusted view of property risk and applying it the same way through underwriting, rating, claims, portfolio management, and reinsurance. The result is enterprise-grade data quality that holds up under reinsurer scrutiny. 

You can see the shift in how carriers go to market. The submission a cedent brings to reinsurers is a tight document covering financial highlights, key initiatives, exposure updates, and CAT loss updates, and data enrichment now sits among those headline sections rather than as a footnote.

What this unlocks is a virtuous lifecycle. Carriers sharpen their view of risk, make better risk selection decisions, capture appropriate rate, and underwrite with greater precision. When they bring that same view of risk to their reinsurer, both sides are aligned — giving cedents the clarity to make the right decisions on reinsurance structure, terms, and pricing. The view of risk gets sharper for everyone involved in the transaction. 

The CAT modeling gap is closing

Catastrophe models simulate peril at remarkable resolution, capturing wind fields, fire spread, flood inundation, and seismic intensity. They work best when those peril views are paired with equally granular property inputs, such as roof material captured at the address, building age that matches reality, defensible space measured rather than estimated, and exposures captured at the building rather than averaged across a ZIP code. For most of the industry's history, that level of property detail wasn't available at scale.

Much of this gap comes down to secondary modifiers — characteristics like roof material, roof age, roof condition, defensible space, and surrounding vegetation. These modifiers don’t come packaged with a CAT model, but they have a material impact on stochastic loss results. Without accurate, property-level secondary modifiers, even the most sophisticated model is making assumptions about the very inputs that drive its outputs. With them, carriers and reinsurers get a sharper, more defensible view of modeled losses.

As property-level data flows into reinsurance submissions, CAT modelers are working with the inputs their models were designed for, and reinsurers are pricing risk against a more accurate picture of what they're actually covering.

Purpose-built AI is the only kind that moves insurance forward 

Another theme that resonated: generic AI doesn’t move insurance forward. Purpose-built AI does. By “purpose-built,” Kevin means AI engineered for a specific insurance problem — replacing something expensive, slow, or inconsistent (manual inspections, piecemeal public-records pulls, or subjective desktop reviews) with something faster, more consistent, and uniformly applied to every property. 

The bar for adoption is simple: the AI’s output has to tie directly to claims outcomes. If an underwriter, actuary, or chief risk officer can see how a capability sharpens loss prediction, it earns its place in the workflow. If they can’t, no amount of polish makes it useful. 

What's next

There's a lot still ahead, including new markets to enter, new perils to model, and deeper integration into the systems where carriers and reinsurers actually make decisions.

Insurance is 700 years old, and the next decade is its most consequential yet. Property risk modeling sits at the center of that decade, combining technical and commercial work at the intersection of science and capital. 

Want to learn more about how ZestyAI helps carriers, brokers, and reinsurers optimize reinsurance decisions? Learn more about ZestyAI’s reinsurance solutions →