Models used for Wildfire catastrophe insurance as of today are not considering substantial information, such as geographic information and environmental constraints. The objective of the project is to establish a theoretical framework and an empirical process to enhance Aviva Canada’s current Wildfire Economic Capital (EC) model, to be able to determine the amount of capital needed to be allocated to ensure the company remains solvent, in case of occurrence of risks.
Accurate forecasting is of crucial importance in managing insurance risks and ensuring a solvent and profitable operation. In recent years the property/Casualty insurance industry has adopted generalized linear models (GLMs) to improve the fit and prediction accuracy of their insurance portfolio models. Yet, the interdependence between the different insurance covers included in packaged products, such as car insurance, need to be explained in the GLM in order to include them in the predictive process.
Information regarding the likelihood and severity of automobile accidents for individual insurance consumers enables insurance companies to price insurance policies. The more accurate and predictive the data, the more accurate and fair insurance prices are. Modern advances, such as telematics technology, allow insurers to improve the quality of their information. This improved information has an efficiency-enhancing effect on the personal automobile insurance market which makes both insurers and consumers better off.
Scientists believe that global warming will trigger increasingly more frequent and violent storms, heat waves, flooding, tornadoes and cyclones in some areas of the globe, while other areas will slip into cold or drought. Although the effects of climate change will impact every segment of the business community, the insurance industry is especially at risk. Extreme weather events in past years have caused tens of billions of dollars in losses for insurers.
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