Online Program Home
  My Program

Abstract Details

Activity Number: 280 - Climate Statistics: Studies on the Physics and Impacts of Climate Change Using Data Science
Type: Topic Contributed
Date/Time: Tuesday, August 1, 2017 : 8:30 AM to 10:20 AM
Sponsor: Section on Physical and Engineering Sciences
Abstract #324125
Title: Modeling Weather-Induced Home Insurance Risks with Support Vector Machine Regression
Author(s): Vyacheslav Lyubchich* and Yulia R. Gel and Asim Kumer Dey
Companies: University of Maryland Center for Environmental Science and University of Texas at Dallas and University of Texas at Dallas
Keywords: machine learning ; climate change ; insurance loss ; forecast ; climate model

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.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2017 program

Copyright © American Statistical Association