A Machine Learning Approach to Predict Health Insurance Claims

Detalhes bibliográficos
Autor(a) principal: Cordeiro, Miguel Filipe Martins
Data de Publicação: 2023
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/148525
Resumo: Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business Analytics
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spelling A Machine Learning Approach to Predict Health Insurance ClaimsClaim ForecastingEnsembleHealth InsuranceMachine LearningTailor Made PoliciesXGBoostDomínio/Área Científica::Ciências Sociais::Economia e GestãoDomínio/Área Científica::Ciências Naturais::Ciências da Computação e da InformaçãoInternship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business AnalyticsRenewing health insurance contracts is, usually, an annual process, in the Tailor Made policies branch. At Multicare, this process starts three months before the end of the clients’ annuity, by estimating the costs of the last quarter using information from the first three. This estimation process is critical to the renewal of insurance contracts, since, if the estimation is too high, the client will overpay for their insurance and might seek more competitive alternatives. In contrast, if the predictions are too low, it will result in losses for the company. This part of the renewal process is currently performed by a time series algorithm, specifically an ARIMA model. This project aims to build a machine learning-based model that will provide more accurate estimations of the claims’ cost and frequency, in the Inpatient coverage, to Multicare. Several algorithms were tested: Linear and Logistic Regressions, Decision Trees, Random Forests, Gradient Boosting and XGBoost; and their results were then compared to the ones of the current ARIMA model. This study showed that a machine learning technique, the XGBoost, is more powerful than the ARIMA, as it projects 9% above the real costs, against the ARIMA’s global error of -25%. These conclusions can lead to changes in Multicare’s approach to predicting claim costs and, consequentially, its way of doing business.António, Nuno Miguel da ConceiçãoRUNCordeiro, Miguel Filipe Martins2023-02-02T08:56:50Z2023-01-232023-01-23T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/148525TID:203210417enginfo: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:RCAAP2024-03-11T05:30:07Zoai:run.unl.pt:10362/148525Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:53:23.801274Repositório 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 A Machine Learning Approach to Predict Health Insurance Claims
title A Machine Learning Approach to Predict Health Insurance Claims
spellingShingle A Machine Learning Approach to Predict Health Insurance Claims
Cordeiro, Miguel Filipe Martins
Claim Forecasting
Ensemble
Health Insurance
Machine Learning
Tailor Made Policies
XGBoost
Domínio/Área Científica::Ciências Sociais::Economia e Gestão
Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação
title_short A Machine Learning Approach to Predict Health Insurance Claims
title_full A Machine Learning Approach to Predict Health Insurance Claims
title_fullStr A Machine Learning Approach to Predict Health Insurance Claims
title_full_unstemmed A Machine Learning Approach to Predict Health Insurance Claims
title_sort A Machine Learning Approach to Predict Health Insurance Claims
author Cordeiro, Miguel Filipe Martins
author_facet Cordeiro, Miguel Filipe Martins
author_role author
dc.contributor.none.fl_str_mv António, Nuno Miguel da Conceição
RUN
dc.contributor.author.fl_str_mv Cordeiro, Miguel Filipe Martins
dc.subject.por.fl_str_mv Claim Forecasting
Ensemble
Health Insurance
Machine Learning
Tailor Made Policies
XGBoost
Domínio/Área Científica::Ciências Sociais::Economia e Gestão
Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação
topic Claim Forecasting
Ensemble
Health Insurance
Machine Learning
Tailor Made Policies
XGBoost
Domínio/Área Científica::Ciências Sociais::Economia e Gestão
Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação
description Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business Analytics
publishDate 2023
dc.date.none.fl_str_mv 2023-02-02T08:56:50Z
2023-01-23
2023-01-23T00:00:00Z
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/10362/148525
TID:203210417
url http://hdl.handle.net/10362/148525
identifier_str_mv TID:203210417
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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