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Activity Number:
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280
- Climate Statistics: Studies on the Physics and Impacts of Climate Change Using Data Science
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Type:
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Topic Contributed
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Date/Time:
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Tuesday, August 1, 2017 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Physical and Engineering Sciences
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Abstract #324125
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Title:
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Modeling Weather-Induced Home Insurance Risks with Support Vector Machine Regression
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Author(s):
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Vyacheslav Lyubchich* and Yulia R. Gel and Asim Kumer Dey
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Companies:
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University of Maryland Center for Environmental Science and University of Texas at Dallas and University of Texas at Dallas
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Keywords:
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machine learning ;
climate change ;
insurance loss ;
forecast ;
climate model
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Abstract:
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Insurance industry is one of the sectors most vulnerable 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 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. As a case study, we model and predict dynamics of weather-induced home insurance claims in a Canadian city.
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Authors who are presenting talks have a * after their name.
