Portfolio rule‐based clustering at automobile insurance in Portugal

Detalhes bibliográficos
Autor(a) principal: Devi, Octaviani
Data de Publicação: 2016
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/19788
Resumo: Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics
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spelling Portfolio rule‐based clustering at automobile insurance in PortugalAutomobile insuranceRule‐based clusteringK‐meansClusteringClassificationDecision treeInternship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsDefining pricing strategy is a challenge for every insurance company. Competition makes insurers need to be more careful to adjust the premium since it may affect the reaction of the existing customer or the new ones. Correspondingly, it may impact the relationship with customer, also the profitability of the company. Moreover, the increment of number of policies will lead to the diversity of policy’s risk profile and characteristics which becomes a challenge for insurer to manage their portfolio. Therefore, a deep understanding of portfolio segmentation is important for the company to fine tune the pricing strategy and gain more profit. The project aims to discover portfolio clusters by using k‐means clustering algorithm and extract the rules of each cluster by developing classification model using Decision Tree algorithm. The result of the model shows that the clusters give different characteristics and behavior. Complement with KPI metrics, the company is able to monitor the performance of each clusters. So that, the company may use the analyses to optimize the strategy of growth and profitability.Henriques, Roberto André PereiraRUNDevi, Octaviani2017-01-16T14:36:28Z2016-07-012016-07-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/19788TID:201284910enginfo: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:RCAAP2024-03-11T04:01:59Zoai:run.unl.pt:10362/19788Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:25:41.796852Repositório 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 Portfolio rule‐based clustering at automobile insurance in Portugal
title Portfolio rule‐based clustering at automobile insurance in Portugal
spellingShingle Portfolio rule‐based clustering at automobile insurance in Portugal
Devi, Octaviani
Automobile insurance
Rule‐based clustering
K‐means
Clustering
Classification
Decision tree
title_short Portfolio rule‐based clustering at automobile insurance in Portugal
title_full Portfolio rule‐based clustering at automobile insurance in Portugal
title_fullStr Portfolio rule‐based clustering at automobile insurance in Portugal
title_full_unstemmed Portfolio rule‐based clustering at automobile insurance in Portugal
title_sort Portfolio rule‐based clustering at automobile insurance in Portugal
author Devi, Octaviani
author_facet Devi, Octaviani
author_role author
dc.contributor.none.fl_str_mv Henriques, Roberto André Pereira
RUN
dc.contributor.author.fl_str_mv Devi, Octaviani
dc.subject.por.fl_str_mv Automobile insurance
Rule‐based clustering
K‐means
Clustering
Classification
Decision tree
topic Automobile insurance
Rule‐based clustering
K‐means
Clustering
Classification
Decision tree
description Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics
publishDate 2016
dc.date.none.fl_str_mv 2016-07-01
2016-07-01T00:00:00Z
2017-01-16T14:36:28Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/19788
TID:201284910
url http://hdl.handle.net/10362/19788
identifier_str_mv TID:201284910
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 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
repository.mail.fl_str_mv
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