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

• URL: http://arxiv.org/abs/2103.08761v1
• Date: Mon, 15 Mar 2021 23:13:32 GMT
• Title: Modeling Weather-induced Home Insurance Risks with Support Vector Machine Regression
• Authors: Asim K. Dey, Vyacheslav Lyubchich, and Yulia R. Gel
• Abstract summary: Insurance industry is one of the most vulnerable sectors to climate change. We study the effect of precipitation on a joint dynamics of weather-induced home insurance claims and losses.
• Score: 1.776495509141596
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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