Machine Learning in P&C Insurance: A Review for Pricing and Reserving

Christopher Blier-Wong, Hélène Cossette, Luc Lamontagne and Etienne Marceau
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Christopher Blier-Wong: École d’actuariat, Université Laval, Québec, QC G1V 0A6, Canada
Hélène Cossette: École d’actuariat, Université Laval, Québec, QC G1V 0A6, Canada
Luc Lamontagne: Département d’informatique et de génie logiciel, Université Laval, Québec, QC G1V 0A6, Canada
Etienne Marceau: École d’actuariat, Université Laval, Québec, QC G1V 0A6, Canada

Risks, 2020, vol. 9, issue 1, 1-26

Abstract: In the past 25 years, computer scientists and statisticians developed machine learning algorithms capable of modeling highly nonlinear transformations and interactions of input features. While actuaries use GLMs frequently in practice, only in the past few years have they begun studying these newer algorithms to tackle insurance-related tasks. In this work, we aim to review the applications of machine learning to the actuarial science field and present the current state of the art in ratemaking and reserving. We first give an overview of neural networks, then briefly outline applications of machine learning algorithms in actuarial science tasks. Finally, we summarize the future trends of machine learning for the insurance industry.

Keywords: machine learning; ratemaking; reserving; property and casualty insurance; neural networks (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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