Modelling Motor Insurance Claim Frequency and Severity Using Gradient Boosting
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , |
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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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 |
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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 |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799138152427814912 |