On Prediction of Future Insurance Claims When the Model Is Uncertain

By Liang Hong, Todd Kuffner, Ryan Martin

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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 first to use some kind of model selection tool to select a “best model” and then to 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 this theory ignores the selection effect. Since accuracy of predictions is crucial to the insurer’s pricing and solvency, care is needed to develop valid prediction methods. This paper investigates the effects of model selection on the validity of classical prediction tools and makes some recommendations for practitioners.

Keywords: Bootstrap; post-selection inference; predictive distribution; regression; variable selection


Hong, Liang, Todd Kuffner, and Ryan Martin, "On Prediction of Future Insurance Claims When the Model Is Uncertain," Variance 12:1, 2018, pp. 90-99.

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Variance (ISSN 1940-6452) is a peer-reviewed journal published by the Casualty Actuarial Society to disseminate work of interest to casualty actuaries worldwide. The focus of Variance is original practical and theoretical research in casualty actuarial science. Significant survey or similar articles are also considered for publication. Membership in the Casualty Actuarial Society is not a prerequisite for submitting papers to the journal and submissions by non-CAS members is encouraged.