Regression Tree Credibility Model

The North American Actuarial Journal, Forthcoming

40 Pages Posted: 13 Dec 2018 Last revised: 10 May 2019

See all articles by Liqun Diao

Liqun Diao

University of Waterloo

Chengguo Weng

University of Waterloo

Date Written: August 21, 2018

Abstract

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

Diao, Liqun and Weng, Chengguo, Regression Tree Credibility Model (August 21, 2018). The North American Actuarial Journal, Forthcoming . Available at SSRN: https://ssrn.com/abstract=3288661 or http://dx.doi.org/10.2139/ssrn.3288661

Liqun Diao

University of Waterloo ( email )

Waterloo, Ontario N2L 3G1
Canada

Chengguo Weng (Contact Author)

University of Waterloo ( email )

M3-200 Univ Ave W
Waterloo, Ontario N2L3G1
Canada
(1)888-4567 ext.31132 (Phone)

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