Aon has launched its latest catastrophe model to analyse severe convective storms in the US.
This is a pressing cause as it has accounted for more then 60% of global natural catastrophe losses in the first half of 2024. According to Aon, US severe convective insured losses in 2023 were more than $58bn.
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Developed by Aon’s Impact Forecasting team, the model reflects re/insurers’ true exposures to the peril, capturing the full spectrum of event severities, spanning higher frequency $1bn to $5bn industry loss events, smaller events that do not appear in the historical record, and realistic tail losses from derechos and other significant severe convective storm hazards.
Also available is a complete 20-year historical event set, allowing companies to validate modeled industry losses, analyse historical events based on their current portfolios for better reinsurance decision making and compare modeled historical losses to claims experience for a more informed view of risk.
Further features include:
- Alignment with recent historical loss experience across industry and client portfolios;
- Average annual loss and low return period aggregate loss estimates that more accurately represent trended client experience;
- Full capture of all severe convective storm sub-perils and appropriate geographic distribution in modeling to allow for a more accurate view of tail-risk;
- A ‘ground-up’ component-based vulnerability framework that explicitly accounts for the cost, resistance and potential damage to different parts of the structure, and
- Open, transparent, and customisable loss modeling platform with vulnerability curves that can be refined to best reflect clients’ unique claims experience and loss histories for a custom view of risk.
Adam Podlaha, CEO of Impact Forecasting, said: “The industry has faced many challenges when attempting to model severe convective storms, especially in terms of producing accurate average annual loss estimates. By using our SCS model, re/insurers will be presented with a more in-depth view of the risks associated with hail, thunderstorm-produced winds and tornadoes, which in turn will help to shape better strategic decisions around portfolio management for this very costly peril.”
Eric Robinson, global SCS model development lead for Impact Forecasting, added: “Another historic year for SCS-related losses again highlights the need for an improved understanding of SCS loss drivers. Improved portfolio management is crucial to helping insurers mitigate growing SCS-related losses, and this can be achieved through modeling solutions that are driven by the latest and ongoing research in atmospheric sciences that helps insurers to obtain additional, valuable insight into evolving SCS risk.”
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