Modelling Motor Insurance Claim Frequency and Severity Using Gradient Boosting

Detalhes bibliográficos
Autor(a) principal: Clemente, Carina
Data de Publicação: 2023
Outros Autores: Guerreiro, Gracinda R., Bravo, Jorge M.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/157779
Resumo: Clemente, C., Guerreiro, G. R., & Bravo, J. M. (2023). Modelling Motor Insurance Claim Frequency and Severity Using Gradient Boosting. Risks, 11(9), 1-20. [163]. https://doi.org/10.3390/risks11090163 ---This research was funded by national funds through the FCT—Fundação para a Ciência e a Tecnologia, I.P., under the scope of the projects UIDB/00297/2020 and UIDP/00297/2020—Center for Mathematics and Applications—(G.R. Guerreiro) and grants UIDB/04152/2020—Centro de Investigação em Gestão de Informação (MagIC) and UIDB/00315/2020—BRU-ISCTE-IUL—(J.M. Bravo).
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spelling Modelling Motor Insurance Claim Frequency and Severity Using Gradient Boostinggradient boostingNon-life insurance pricingeExpert systemsrisk managementpredictive modellingactuarial sciencAccountingEconomics, Econometrics and Finance (miscellaneous)Strategy and ManagementClemente, C., Guerreiro, G. R., & Bravo, J. M. (2023). Modelling Motor Insurance Claim Frequency and Severity Using Gradient Boosting. Risks, 11(9), 1-20. [163]. https://doi.org/10.3390/risks11090163 ---This research was funded by national funds through the FCT—Fundação para a Ciência e a Tecnologia, I.P., under the scope of the projects UIDB/00297/2020 and UIDP/00297/2020—Center for Mathematics and Applications—(G.R. Guerreiro) and grants UIDB/04152/2020—Centro de Investigação em Gestão de Informação (MagIC) and UIDB/00315/2020—BRU-ISCTE-IUL—(J.M. Bravo).Modelling claim frequency and claim severity are topics of great interest in property-casualty insurance for supporting underwriting, ratemaking, and reserving actuarial decisions. Standard Generalized Linear Models (GLM) frequency–severity models assume a linear relationship between a function of the response variable and the predictors, independence between the claim frequency and severity, and assign full credibility to the data. To overcome some of these restrictions, this paper investigates the predictive performance of Gradient Boosting with decision trees as base learners to model the claim frequency and the claim severity distributions of an auto insurance big dataset and compare it with that obtained using a standard GLM model. The out-of-sample performance measure results show that the predictive performance of the Gradient Boosting Model (GBM) is superior to the standard GLM model in the Poisson claim frequency model. Differently, in the claim severity model, the classical GLM outperformed the Gradient Boosting Model. The findings suggest that gradient boost models can capture the non-linear relation between the response variable and feature variables and their complex interactions and thus are a valuable tool for the insurer in feature engineering and the development of a data-driven approach to risk management and insurance.NOVA Information Management School (NOVA IMS)CMA - Centro de Matemática e AplicaçõesDM - Departamento de MatemáticaInformation Management Research Center (MagIC) - NOVA Information Management SchoolRUNClemente, CarinaGuerreiro, Gracinda R.Bravo, Jorge M.2023-09-13T22:19:35Z2023-09-122023-09-12T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article20application/pdfhttp://hdl.handle.net/10362/157779eng2227-9091PURE: 71474553https://doi.org/10.3390/risks11090163info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:40:06Zoai:run.unl.pt:10362/157779Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:56:52.609782Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Modelling Motor Insurance Claim Frequency and Severity Using Gradient Boosting
title Modelling Motor Insurance Claim Frequency and Severity Using Gradient Boosting
spellingShingle Modelling Motor Insurance Claim Frequency and Severity Using Gradient Boosting
Clemente, Carina
gradient boosting
Non-life insurance pricinge
Expert systems
risk management
predictive modelling
actuarial scienc
Accounting
Economics, Econometrics and Finance (miscellaneous)
Strategy and Management
title_short Modelling Motor Insurance Claim Frequency and Severity Using Gradient Boosting
title_full Modelling Motor Insurance Claim Frequency and Severity Using Gradient Boosting
title_fullStr Modelling Motor Insurance Claim Frequency and Severity Using Gradient Boosting
title_full_unstemmed Modelling Motor Insurance Claim Frequency and Severity Using Gradient Boosting
title_sort Modelling Motor Insurance Claim Frequency and Severity Using Gradient Boosting
author Clemente, Carina
author_facet Clemente, Carina
Guerreiro, Gracinda R.
Bravo, Jorge M.
author_role author
author2 Guerreiro, Gracinda R.
Bravo, Jorge M.
author2_role author
author
dc.contributor.none.fl_str_mv NOVA Information Management School (NOVA IMS)
CMA - Centro de Matemática e Aplicações
DM - Departamento de Matemática
Information Management Research Center (MagIC) - NOVA Information Management School
RUN
dc.contributor.author.fl_str_mv Clemente, Carina
Guerreiro, Gracinda R.
Bravo, Jorge M.
dc.subject.por.fl_str_mv gradient boosting
Non-life insurance pricinge
Expert systems
risk management
predictive modelling
actuarial scienc
Accounting
Economics, Econometrics and Finance (miscellaneous)
Strategy and Management
topic gradient boosting
Non-life insurance pricinge
Expert systems
risk management
predictive modelling
actuarial scienc
Accounting
Economics, Econometrics and Finance (miscellaneous)
Strategy and Management
description Clemente, C., Guerreiro, G. R., & Bravo, J. M. (2023). Modelling Motor Insurance Claim Frequency and Severity Using Gradient Boosting. Risks, 11(9), 1-20. [163]. https://doi.org/10.3390/risks11090163 ---This research was funded by national funds through the FCT—Fundação para a Ciência e a Tecnologia, I.P., under the scope of the projects UIDB/00297/2020 and UIDP/00297/2020—Center for Mathematics and Applications—(G.R. Guerreiro) and grants UIDB/04152/2020—Centro de Investigação em Gestão de Informação (MagIC) and UIDB/00315/2020—BRU-ISCTE-IUL—(J.M. Bravo).
publishDate 2023
dc.date.none.fl_str_mv 2023-09-13T22:19:35Z
2023-09-12
2023-09-12T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/157779
url http://hdl.handle.net/10362/157779
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2227-9091
PURE: 71474553
https://doi.org/10.3390/risks11090163
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 20
application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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