Posted on 10 Jul 13 by Annie George
Impact Forecasting, the catastrophe model development center of excellence at Aon Benfield, has launched a scenario model for the recent Alberta floods in Canada. Using event footprints that outline the extent of the flood enables insurers to obtain a more realistic estimate of their specific exposure – just days after the event. Aon Benfield is the global reinsurance intermediary and capital advisor of Aon plc.
The footprint of the flood event is based on an image from SERTIT distributed by PERILS. The footprint is then manually processed to correct areas originally covered by clouds and converted to ELEMENTS format so it can be uploaded to the Impact Forecasting’s loss calculation platform. In ELEMENTS the hazard is then superimposed onto the insurer’s portfolio to calculate exposed sums insured.
Steve Jakubowski, president of Impact Forecasting, commented: “This scenario model illustrates the global scope of an open platform, such as ELEMENTS, where third party data can be used to quantify clients’ exposure in areas with no existing models available prior to the event."
Martin Kadlec, flood model developer at international Impact Forecasting, added: “For the Alberta floods, ELEMENTS provides more accurate results based on the proportion of sums insured affected rather than the entire sums insured for each administrative unit or postal code. Additionally, the affected sums insured within each area do not blindly follow the location of the floods, but are weighted by population density data. Both of these parameters make this model more beneficial than simple GIS based analyses.”
The footprint is also available on ImpactOnDemand® - Aon Benfield’s highly innovative and versatile platform that enables clients to visualize and quantify their exposures to risk, in addition to performing sophisticated, detailed data analysis to drive insightful business decisions.
The new Alberta tool builds upon Impact Forecasting’s suite of new scenario models, which also enable insurers and reinsurers to validate existing probabilistic models and examine specific events in territories where no models currently exist. In addition, firms can monitor exposure in key areas and provide more detailed information for reinsurance purchase and claims management.