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AI for flood risk.
Floods cause $40 billion in damage each year, yet only 30% of that devastation is insured. Communities without coverage tend to be high risk. They are either priced out of plans, or low quality, fragmented data make it hard to write policies at all. Since insurance access is a major determinant of recovery time after flooding, this is an urgent problem in a future where major storms occur more frequently.
How it Works
Cloud to Street uses best-in-class flood models and direct observation through satellite data to underwrite parametric flood insurance policies. Whereas indemnity insurance pays out against actual losses incurred, parametric insurance is triggered by observable events like the strength of a hurricane, facilitating faster, cheaper, and easier payouts, which is essential in major disasters where time is critical.
Traditional flood models take years to build and rely on sparse, expensive data. Cloud to Street’s direct observation methods measure risk faster and more accurately, enabling insurance policies to be written in markets where traditional data is insufficient for underwriting decisions today. This unlocks billions of dollars in potential premiums that their reinsurance partners have never been able to access.
new people insured through their data
BESSIE SCHWARZ CEO & CO-FOUNDER
Bessie was the founder of the Yale Program on Climate Change Communication Partnerships. She holds an MS from Yale University.
BETH TELLMAN CSO & CO-FOUNDER
An expert in remote sensing for floods, Beth holds a postdoc from Columbia, an MS in Hydrology from Yale, and a PhD in Geography from ASU.
Satellite imaging reveals increased proportion of population exposed to floodsNature
New global map shows populations are growing faster in flood-prone areasMIT Technology Review
These entrepreneurs are democratizing data to predict flood riskGreenBiz
Global Flood DatabaseCloud to Street