Seize the day with better flood risk management tools
Floods are ubiquitous in the United States.
According to the USGS, floods account for more property damage than any other natural disaster and figure into 90% of all federal natural disaster declarations in the country. Make more informed decisions, explore risk financing strategies, and sensitivity test new products with the AIR Inland Flood Model for the U.S.
Inform your view of risk with real-world data.
Thanks to a major study commissioned by FEMA, the AIR U.S inland flood model is the only model to have been extensively validated using more than 30 years of detailed NFIP flood claims.
Meet the needs of a changing market.
The recent, unanimous passage of a flood insurance bill in the House of Representatives could allow for greater privatization of flood insurance and generate new growth opportunities. The AIR Inland Flood Model for the U.S. is a comprehensive tool that helps you identify areas well-suited for private coverage.
Get a realistic view of your flood risk.
From rainfall generation to flash floods and river flows, the probabilistic AIR Inland Flood Model for the U.S. captures all of the complexities that encompass flood risk. Its high resolution enables flood depth estimation at the location level, so you can pinpoint risk drivers, underwrite with confidence, and develop markets where take-up rates are low.
Enhance the accuracy of your analytics.
Leveraging engineering analyses, published research, damage surveys, and claims data, AIR developed separate damage functions for residential and commercial exposures, large industrial facilities, and automobiles.
The model supports secondary building characteristics—such as first floor elevation, foundation type, and flood defenses—which can greatly impact exposure vulnerability and potential losses.
Assess your off-floodplain risk.
Because nearly a third of NFIP flood insurance claims come from properties outside of the 100-year floodplain, the AIR model includes a module for explicit off-floodplain loss estimation.
