Regression Tree Credibility Model
The North American Actuarial Journal, Forthcoming
40 Pages Posted: 13 Dec 2018 Last revised: 10 May 2019
Date Written: August 21, 2018
This paper applies machine learning techniques to credibility theory and proposes a regression-tree-based algorithm to integrate covariate information into credibility premium prediction. The recursive binary algorithm partitions a collective of individual risks into mutually exclusive sub-collectives, and applies the classical Buhlmann-Straub credibility formula for the prediction of individual net premiums. The algorithm provides a flexible way to integrate covariate information into individual net premiums prediction. It is appealing for capturing non-linear and/or interaction covariate effects. It automatically selects influential covariate variables for premium prediction and requires no additional ex-ante variable selection procedure. The superiority in the prediction accuracy of the proposed algorithm is demonstrated by extensive simulation studies. The proposed method is applied to the U.S. Medicare data for illustration purposes.
Keywords: Credibility Theory; Regression Tree; Premium Rating; Predictive Analytics
JEL Classification: C14
Suggested Citation: Suggested Citation