Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/257917 
Year of Publication: 
2019
Citation: 
[Journal:] Risks [ISSN:] 2227-9091 [Volume:] 7 [Issue:] 3 [Article No.:] 79 [Publisher:] MDPI [Place:] Basel [Year:] 2019 [Pages:] 1-18
Publisher: 
MDPI, Basel
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.
Subjects: 
loss reserving
gradient boosting
individual models
predictive modeling
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
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