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1 September 2021Insurance

Swiss Re expands parametric tropical storm insurance with Reask

Swiss Re Corporate Solutions, the commercial insurance arm of  Swiss Re Group, has expanded its parametric tropical cyclone insurance with risk data provider Reask.

Swiss Re has teamed up with tropical cyclone risk analytics firm Reask to expand its parametric windstorm solution, called Storm . It will leverage Reask's advanced technological wind speed data to provide tropical cyclone coverage to corporate and public entities exposed to the peril.

The insurer uses third-party verified wind speed data as a trigger for coverage in its signature parametric Storm solution. It is an insurance tool that pays out quickly and is often used to address traditional marketplace limitations or as a standalone risk transfer option.

Initially launched in hurricane-prone areas of the US and the Caribbean, Storm will now be available in all tropical cyclone-exposed areas worldwide.

Reask uses its globally connected framework and machine learning to provide natural catastrophe risk models for the insurance industry. Metryc, Reask's tropical cyclone risk modelling product, is built to address the specific requirements of nat cat parametric insurance contracts. In areas outside of the US and the Caribbean, Storm will use Metryc to determine whether a policy is triggered.

Martin Hotz (pictured), head of parametric nat cat at Swiss Re Corporate Solutions, said: "Tropical cyclones continue to pose a major risk. Thanks to our collaboration with Reask, organisations around the world will have access to broader coverage, flexible use of funds and a speedier recovery with Storm."

Thomas Loridan, CEO at Reask, added: "Parametric solutions such as Storm help reduce the insurance protection gap. Until now, a lack of reliable observations in many tropical cyclone-affected areas have limited the deployment of such solutions beyond established markets. Our Metryc product provides complete global coverage, augmenting scarcely available observations through predictive modelling."

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