Building it from the property data up: Model quality, secondary perils and global property portfolios

About this event

Catastrophe models are only as good as the property data that is fed into them. The London Market writes some of the world’s most exposed property risk and much of it sits in the US: hurricane, wildfire, severe convective storm, flood and earthquake. A decision made in London depends on how well a portfolio understands American property and how robust the data behind that decision is. As secondary peril volatility and emerging risks squeeze margins across global portfolios, macro-level assumptions are no longer enough.

The weak point is often the property data itself. Models and pricing decisions frequently run on inputs that lack the detail to separate a strong risk from a weak one, particularly for the secondary perils driving today’s losses. The difference between two neighbouring buildings can come down to roof type, construction material or what surrounds them, yet that detail rarely makes it to the model. Poor granularity inflates loss uncertainty exactly where it is hardest to carry, across wildfire, severe convective storm and flood. Keeping that data consistent and high-resolution across cross-border, multi-territory portfolios is harder still.

This networking event, hosted by InsTech in partnership with Cotality, looks at how precise, asset-level property data improves catastrophe model output, reduces uncertainty across multi-territory portfolios and sharpens risk selection. Cotality holds detailed attributes on the US property stock and works across global portfolios and the full cycle from underwriting to rebuild. It is also a chance to look at where AI genuinely helps in property risk and why granular property data remains the foundation that models still rely on.

The discussion will cover:

• Why precise, asset-level property inputs are one of the most direct ways to improve existing catastrophe model output
• How escalating secondary peril losses are reshaping property underwriting on US-exposed portfolios
• How poor data granularity inflates loss uncertainty across secondary and emerging perils
• What it takes to keep property data consistent and high-resolution across cross-border, multi-territory portfolios
• Which property attributes actually separate a strong risk from a weak one
• Where AI adds real value in property risk

Want to find out more about why you should come? Watch a short interview with Steve Hurcom below: