Life Insurance: Predictive Modeling for Life Insurers
Research Projects – Life Insurance
Traditionally predictive modeling techniques have been used within the insurance industry to help gain a better understanding of current and/or future insured risks leading to improved risk segmentation and underwriting, pricing and marketing processes and decisions. Predictive modeling involves "mining" datasets and performing statistical analysis that may uncover unexpected relationships about the underlying risks that may indicate the likelihood of future outcomes for an insurer. While predictive models have been developed and applied within the property and casualty and health insurance industries, their prevalence is not as great within the life insurance industry. The SOA is pleased to share this collection of papers written to examine how existing or new methods or models can be applied in the life insurance industry.
Application of Predictive Models Techniques to Measure Dynamic Policyholder Behavior in Variable Annuity Contracts
by Guillaume Briere-Giroux, Jean-Felix Huet, Robert Spaul, Andy Staudt, and David Weinsier*
Household Life Insurance Demand-a Multivariate Two-Part Model
by Edward W. (Jed) Frees and Yunjie (Winnie) Sun
Predictive Model Learning of Stochastic Simulations
by John Hegstrom*
Predictive Modeling for Life Insurance
by Mike Batty, Arun Tripathi, Alice Kroll, Cheng-sheng Peter Wu, David Moore, Chris Stehno, Lucas Lau , Jim Guszcsa, and Mitch Katcher*
The Committee on Life Insurance Research would like to thank the following members of the Project Oversight Group for their valuable contribution to this project.
- Jeff Beckley
- Craig Buck
- Tony Green
- Jim Miles, SOA staff
- Jan Schuh, SOA staff
- Ronora Stryker, SOA staff
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The thoughts, insights, and opinions expressed and the conclusions reached by the authors are their own and do not represent any official position or opinion of the Society of Actuaries or its members or the authors' employers. The Society of Actuaries makes no representation or warranty to the accuracy of the information.