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Gradient Boosting in Motor Insurance Claim Frequency Modelling

2023-10-21Documento de conferência Acesso aberto
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dc.contributor.authorClemente, Carina de Miranda
dc.contributor.authorGuerreiro, Gracinda Rita Diogo
dc.contributor.authorBravo, Jorge Miguel
dc.contributor.institutionNOVA Information Management School (NOVA IMS)
dc.contributor.institutionCMA - Centro de Matemática e Aplicações
dc.contributor.institutionFaculdade de Ciências e Tecnologia (FCT)
dc.contributor.institutionInformation Management Research Center (MagIC) - NOVA Information Management School
dc.date.accessioned2024-02-09T00:23:07Z
dc.date.available2024-02-09T00:23:07Z
dc.date.issued2023-10-21
dc.descriptionClemente, C. D. M., Guerreiro, G. R. D., & Bravo, J. M. (2023). Gradient Boosting in Motor Insurance Claim Frequency Modelling. In CAPSI 2023 Proceedings (pp. 53-69). Article 5 (Atas da Conferência da Associação Portuguesa de Sistemas de Informação). Associação Portuguesa de Sistemas de Informação. https://doi.org/10.18803/capsi.v23.53-69 --- 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).
dc.description.abstractModelling claim frequency and claim severity are topics of great interest in property-casualty insurance for supporting underwriting, ratemaking, and reserving actuarial decisions. This paper investigates the predictive performance of Gradient Boosting with Decision Trees as base learners to model the claim frequency in motor insurance using a private cross-country large insurance dataset. The Gradient Boosting algorithm combines many weak base learners to tackle conceptual uncertainty in empirical research. The findings show that the Gradient Boosting model is superior to the standard Generalised Linear Model in the sense that it provides closer predictions in the claim frequency model. The finding also shows that Gradient Boosting can capture the nonlinear relation between the claim counts and feature variables and their complex interactions being, thus, a valuable tool for feature engineering and the development of a data-driven approach to risk management.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent18
dc.format.extent680731
dc.identifier.doi10.18803/capsi.v23.53-69
dc.identifier.issn2183-489X
dc.identifier.otherPURE: 83055137
dc.identifier.otherPURE UUID: 889e1a2f-0bfd-418f-8d47-68b61c05d47e
dc.identifier.otherScopus: 85187545973
dc.identifier.otherORCID: /0000-0002-7389-5103/work/152758649
dc.identifier.otherORCID: /0000-0003-4805-2638/work/218920027
dc.identifier.urihttp://hdl.handle.net/10362/163312
dc.identifier.urlhttps://www.scopus.com/pages/publications/85187545973
dc.language.isoeng
dc.peerreviewedyes
dc.publisherAPSI - Associação Portuguesa de Sistemas de Informação
dc.relationinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04152%2F2020/PT
dc.relationInformation Management Research Center
dc.relationCenter for Mathematics and Applications
dc.relationCenter for Mathematics and Applications
dc.subjectGradient Boosting
dc.subjectNon-life Insurance Pricing
dc.subjectExpert systems
dc.subjectPredictive modelling
dc.subjectRisk Management
dc.subjectInformation Systems and Management
dc.subjectManagement Information Systems
dc.subjectManagement of Technology and Innovation
dc.subjectInformation Systems
dc.subjectComputer Science Applications
dc.titleGradient Boosting in Motor Insurance Claim Frequency Modellingen
dc.typeconference object
degois.publication.firstPage53
degois.publication.lastPage69
degois.publication.titleCAPSI 2023 Proceedings
degois.publication.title23.ª Conferência da Associação Portuguesa de Sistemas de Informação
dspace.entity.typePublication
oaire.awardNumberUIDB/04152/2020
oaire.awardNumberUIDP/00297/2020
oaire.awardNumberUIDB/00297/2020
oaire.awardTitleInformation Management Research Center
oaire.awardTitleCenter for Mathematics and Applications
oaire.awardTitleCenter for Mathematics and Applications
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04152%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F00297%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00297%2F2020/PT
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccess
relation.isProjectOfPublication3274bdb3-4dd3-4bbe-8f74-d34190081f87
relation.isProjectOfPublication65d392f7-8781-4d70-b9f3-069b07d4a311
relation.isProjectOfPublicationd00ae22f-ec2b-47b2-935e-60cb44493cc6
relation.isProjectOfPublication.latestForDiscovery3274bdb3-4dd3-4bbe-8f74-d34190081f87

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