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Loss Reserving Models: Granular and Machine Learning Forms
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Individual Loss Reserving Using a Gradient Boosting-Based Approach

Quantact/Département de Mathématiques, Université du Québec à Montréal (UQAM), Montreal, QC H2X 3Y7, Canada
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These authors contributed equally to this work.
Risks 2019, 7(3), 79; https://doi.org/10.3390/risks7030079
Received: 30 May 2019 / Revised: 27 June 2019 / Accepted: 5 July 2019 / Published: 12 July 2019
(This article belongs to the Special Issue Claim Models: Granular Forms and Machine Learning Forms)
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. View Full-Text
Keywords: loss reserving; predictive modeling; individual models; gradient boosting loss reserving; predictive modeling; individual models; gradient boosting
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Duval, F.; Pigeon, M. Individual Loss Reserving Using a Gradient Boosting-Based Approach. Risks 2019, 7, 79.

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