Modeling Weather-induced Home Insurance Risks with Support Vector Machine Regression
This work addresses risk management for the insurance industry, which is vulnerable to climate change, but appears incremental as it applies existing methods to a specific domain.
The researchers tackled the problem of forecasting weather-induced home insurance claims and losses by applying Support Vector Machine regression and Artificial Neural Networks to precipitation data, demonstrating their approach on a case study in a Canadian Prairies city.
Insurance industry is one of the most vulnerable sectors to climate change. Assessment of future number of claims and incurred losses is critical for disaster preparedness and risk management. In this project, we study the effect of precipitation on a joint dynamics of weather-induced home insurance claims and losses. We discuss utility and limitations of such machine learning procedures as Support Vector Machines and Artificial Neural Networks, in forecasting future claim dynamics and evaluating associated uncertainties. We illustrate our approach by application to attribution analysis and forecasting of weather-induced home insurance claims in a middle-sized city in the Canadian Prairies.