Modeling Weather-induced Home Insurance Risks with Support Vector Machine Regression

TL;DR

This paper explores using Support Vector Machine regression to model and forecast weather-induced home insurance claims and losses, emphasizing precipitation effects and uncertainty evaluation in a Canadian city.

Contribution

It introduces a novel application of SVM regression for weather-related insurance risk modeling and compares its utility with neural networks.

Findings

SVM effectively predicts claim dynamics with reasonable accuracy.

Precipitation significantly influences insurance claims and losses.

The approach aids in better risk assessment and disaster preparedness.

Abstract

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.