Liang Hong

On prediction of future insurance claims when the model is uncertain

Date: 10/19/2018
Time: 3:30PM-4:30PM
Place: 315 Armstrong Hall

Abstract: Predictive modeling is arguably one of the most important tasks actuaries face in their day-to-day work. In practice, actuaries may have a number of reasonable models to consider, all of which will provide different predictions. The most common strategy is to first use some kind of model selection tool to select a ``best model,'' and then use that model to make predictions. However, there is reason to be concerned about the use of the classical distribution theory to develop predictions because these ignore the selection effect. Since accuracy of predictions is crucial to the insurer's pricing and solvency, care is needed to develop valid prediction methods. In this talk, we undertake an investigation of the effects of model selection on the validity of classical prediction tools and make some recommendations for practitioners.

Liang Hong

Dr. Hong received his PhD in mathematics from Purdue University. He has received grants from the Casualty Actuarial Society (CAS), Society of Actuaries (SOA), and State Farm Insurance Company; and he has given research talks at several Center for Actuarial Excellence (CAE) schools including Drake University, Georgia State University, Temple University, University of Waterloo, University of Wisconsin, Madison.

All are welcome.

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