Policy renewal optimization project in health insurance

Detalhes bibliográficos
Autor(a) principal: Bayoudh, Mohamed Ali
Data de Publicação: 2021
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/112795
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 Policy renewal optimization project in health insuranceRenewalPropensityChurnLiftROCDataSoftwareSASEMBLEMTower WatsonHealth InsuranceInternship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsThe Renewal Optimization has been developed over the course of 9 months (September 2018 till June 2019) as part of the thesis for the MSc in Advanced Analytics and Data Science at the Nova Information Management School (Universidade Nova de Lisboa). The objective was to Predict the Probability of Renewing the policy of our customer. This would allow for more assertive and targeted marketing actions and decision making as well as fine tune the pricing strategy. The Training Sample is composed of data from the 1st of January 2017 till 30 June 2018 and the results presented reflect a picture of the Médis individual client portfolio from July 1st, 2018 till 31 December 2018 with 68 732 policies tested. The attributes used in the modelling process cover 6 customer dimensions: demographic, customer profile, product profile, bank variables and usage as well as interaction with the company. The final model results calculated the Renewal Probabilities of every active policy. These calculations have been divided in deciles where the first group have the lowest Renewal Probability estimated and the last one has the highest Renewal Probability estimated. To determine the factor that affects the Renewal Rate the most, a comparison has been conducted between the first and the last group (low probability and high probability groups). Next steps for the project include, but are not limited to, making the results available to all stakeholders and the monitoring plan is also discussed.Pinheiro, Flávio Luís PortasRUNBayoudh, Mohamed Ali2021-03-01T15:59:10Z2021-02-092021-02-09T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/112795TID:202654745enginfo: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:RCAAP2024-03-11T04:56:09Zoai:run.unl.pt:10362/112795Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:42:13.118099Repositório 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 Policy renewal optimization project in health insurance
title Policy renewal optimization project in health insurance
spellingShingle Policy renewal optimization project in health insurance
Bayoudh, Mohamed Ali
Renewal
Propensity
Churn
Lift
ROC
Data
Software
SAS
EMBLEM
Tower Watson
Health Insurance
title_short Policy renewal optimization project in health insurance
title_full Policy renewal optimization project in health insurance
title_fullStr Policy renewal optimization project in health insurance
title_full_unstemmed Policy renewal optimization project in health insurance
title_sort Policy renewal optimization project in health insurance
author Bayoudh, Mohamed Ali
author_facet Bayoudh, Mohamed Ali
author_role author
dc.contributor.none.fl_str_mv Pinheiro, Flávio Luís Portas
RUN
dc.contributor.author.fl_str_mv Bayoudh, Mohamed Ali
dc.subject.por.fl_str_mv Renewal
Propensity
Churn
Lift
ROC
Data
Software
SAS
EMBLEM
Tower Watson
Health Insurance
topic Renewal
Propensity
Churn
Lift
ROC
Data
Software
SAS
EMBLEM
Tower Watson
Health Insurance
description Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics
publishDate 2021
dc.date.none.fl_str_mv 2021-03-01T15:59:10Z
2021-02-09
2021-02-09T00:00:00Z
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/10362/112795
TID:202654745
url http://hdl.handle.net/10362/112795
identifier_str_mv TID:202654745
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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