Parametric flood policies - Insurers no longer in uncharted waters?

So far in the 2023/24 season, the UK has experienced ten named storms, the second highest figure since storm naming began in 2015/16. These storms have caused significant flooding across the country. 

Classed as a secondary peril, flooding is expected to generate small to mid-sized losses, following other extreme ‘primary’ weather events. However, recent flooding has seen towns submerged under water, travel disrupted and homes and businesses ruined. Further, up to 80% of the world’s catastrophic flood losses are uninsured and the lack of local measurements for flood data is increasing the protection gap for countries without access to the technology. 

How can parametric policies insure flood associated risks?

Parametric policies can provide coverage for flood associated risks, as they can automatically pay out upon the occurrence of certain events, such as where a flood reaches a specified level. For further insight into the background of parametric policies, please see our previous article on this topic.

FloodFlash, a parametric insurance specialist for commercial flood insurance, was founded following the difficulties in renewing flood cover after Hurricane Sandy in 2012.

Data is key to these policies, with FloodFlash using sensors at the insured’s property to measure and receive depth data. The sensors are low in cost, meaning they can often be installed at an insured’s property within the costs of the policy. Using the local measurements provided by the sensor, the risk can be measured without having to rely on other general measures alone, such as satellite data. The risk can also be measured in real time. 

Speaking on the increase in use of airborne sensors in addition to previously used terrain data, Ian Bartholomew, Chief Underwriting Officer at FloodFlash said:

“Having that data available is vastly improving the quality of the flood maps that we have access to,” “and computational power has reached a point where you can make use of that data. It’s why we’re seeing pretty rapid improvements in the quality of flood models.”

Parametric policies may also provide lower premiums and coverage for risks that are traditionally excluded, as the sensor data can be utilised to provide certainty around the risks, rather than areas to exclude. Through prior agreed claims payment figures and pre-defined depth measured by the sensor, the policies allow for quicker, automated claims payments to be made and claims professionals to be removed from the process. FloodFlash’s fastest claim is reported to have been paid 3 hours and 50 minutes after the water reached trigger depth at the insured’s property. Looking to the future, FloodFlash are looking to expand their scope beyond traditional property sections to meet client needs.

“We’re championing the use of low-cost sensors to enable fast insurance payments. We’re pushing the application of national scale flood models to underwrite parametric flood policies wherever it is possible. And we’re using cloud platforms to effectively create a turnkey solution that partners can feed into” - Ian Bartholomew, Chief Underwriting Officer at FloodFlash.

Further flood prediction developments

Other organisations have been using AI to predict flooding, protect individuals and assist businesses. 7Analytics uses software that considers the geography of the land and rivers, how built up an area is and its drainage capacity, alongside the weather, to provide real-time predictions. Whilst Neara creates digital flood simulations to allow the electricity infrastructure industry to prepare for and minimise damage caused by flooding. Google has also created a Flood Hub that uses satellite imagery and AI to simulate when rivers may flood following heavy rainfall. However, using to AI to predict flooding still has its limits, as where there is a lack of flood data, such as in areas that rarely flood, the accuracy of the predictions may be reduced.

Therefore, with flood predictions developing and “several risks on the horizon that lend themselves to parametric placements”, we may continue to see an increase in parametric policies providing quick and efficient coverage to risks that were once uncertain.