CAR’s approach to catastrophe modelling has in large part been developed in co-operation with insurance clients and partners and so reflects the needs of the UK insurance and international reinsurance industries, in terms of the range of perils and territories covered and the focus on loss estimation for buildings, contents and business interruption. CAR’s models use industry-standard geocoding, such as postcode, administrative unit or CRESTA zone, to provide country-specific loss estimates for large residential, commercial and industrial property portfolios.
These outputs can be used by reinsurance companies to price their reinsurance treaties or by primary insurers and their brokers to control their exposure and optimise their reinsurance purchasing. CAR’s models are not black boxes, but are designed to be easily integrated with clients’ existing in-house or third party models, or to be used on a standalone basis.
CAR has experience of modelling the vulnerability of the built environment to a wide range of natural and man-made perils, including the following:
Tropical and temperate windstorm
Riverine flood, storm surge and tsunami
Unexploded Ordnance (UXO)
Territorial coverage (Earthquake)
CAR has developed vulnerability models for 35 countries and territories across 5 continents, covering most of the countries of primary interest to the international insurance and reinsurance industries.
The Global Event Vulnerability Estimation System (GEVES) is a model for estimating losses to property portfolios from catastrophe events. Designed for insurance and reinsurance applications, GEVES is designed to be integrated with clients’ existing hazard models to upgrade their loss estimation capability, or to be used on a standalone basis to estimate losses from a given event.
Initially focused on earthquake risk, the GEVES model range has in recent years been broadened to a multi-peril capability with the development of typhoon vulnerability models for China and Japan. These models utilise the underlying GEVES structure and methodology but adapt them to the requirements of windstorm modelling.