Individual Loss Reserving Using a Gradient Boosting-Based Approach

Francis Duval and Mathieu Pigeon
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Francis Duval: Quantact/Département de Mathématiques, Université du Québec à Montréal (UQAM), Montreal, QC H2X 3Y7, Canada
Mathieu Pigeon: Quantact/Département de Mathématiques, Université du Québec à Montréal (UQAM), Montreal, QC H2X 3Y7, Canada

Risks, 2019, vol. 7, issue 3, 1-18

Abstract: In this paper, we propose models for non-life loss reserving combining traditional approaches such as Mack’s or generalized linear models and gradient boosting algorithm in an individual framework. These claim-level models use information about each of the payments made for each of the claims in the portfolio, as well as characteristics of the insured. We provide an example based on a detailed dataset from a property and casualty insurance company. We contrast some traditional aggregate techniques, at the portfolio-level, with our individual-level approach and we discuss some points related to practical applications.

Keywords: loss reserving; predictive modeling; individual models; gradient boosting (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

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Persistent link: https://EconPapers.repec.org/RePEc:gam:jrisks:v:7:y:2019:i:3:p:79-:d:247985

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