  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.
