Understanding and predicting lapses in mortgage life insurance using a machine learning approach

Detalhes bibliográficos
Autor(a) principal: Manteigas, Carlos Manuel Andrade
Data de Publicação: 2024
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/164918
Resumo: Project Work presented as the partial requirement for obtaining a Master's degree in Data Driven Marketing, specialization in Data Science for Marketing
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spelling Understanding and predicting lapses in mortgage life insurance using a machine learning approachMortgage life insuranceLapse riskMachine learningExternal Data SourcesSDG 3 - Good health and well-beingSDG 4 - Quality educationSDG 9 - Industry, innovation and infrastructureSDG 12 - Responsible production and consumptionSDG 13 - Climate actionDomínio/Área Científica::Ciências Naturais::Ciências da Computação e da InformaçãoProject Work presented as the partial requirement for obtaining a Master's degree in Data Driven Marketing, specialization in Data Science for MarketingMortgage life insurance (MLI) offers lucrative opportunities for insurers in Portugal but retaining customers has become challenging amid regulatory changes and fierce competition. After 2009, the market has been reshaped by new competitors with aggressive low premium strategies, posing difficulties for conventional insurers and banks to retain MLI customers. Increasing policy cancellations have become a pressing concern for these established financial institutions. To address this complex and adverse context, this study has tried to develop a predictive model to identify MLI policies prone to lapse and unravel the underlying factors driving this behavior. Using a powerful combination of data from an insurance company and its affiliated bank, the capabilities of four different machine learning (ML) models were explored: Logistic Regression, Random Forest, Neural Networks and XGBoost, with the latter showing the most consistent results. Although the model performs reasonably well, the difficulty in balancing model complexity and generalizability and optimizing Precision and Recall reveals room for improvement. Two novel approaches were introduced by narrowing the focus of the study to one specific insurance protection product, the MLI, and by integrating bank data, to capture multidimensional drivers of lapse behavior, and emphasizing the value of a holistic perspective. Applying SHAP enhanced the interpretability of XGBoost by identifying and explaining the most influential features affecting the predictive model. Notably, the value of external data is underscored as the top four features originated from the bank data. From the insurance company's point of view, this study introduces advanced ML techniques to improve the accuracy of policy lapse prediction, allowing the company to identify and target customers at risk of lapse proactively.António, Nuno Miguel da ConceiçãoRUNManteigas, Carlos Manuel Andrade2024-03-14T15:27:04Z2024-02-022024-02-02T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/164918TID:203544986enginfo: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:RCAAP2024-03-18T01:47:42Zoai:run.unl.pt:10362/164918Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T04:02:04.770554Repositório 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 Understanding and predicting lapses in mortgage life insurance using a machine learning approach
title Understanding and predicting lapses in mortgage life insurance using a machine learning approach
spellingShingle Understanding and predicting lapses in mortgage life insurance using a machine learning approach
Manteigas, Carlos Manuel Andrade
Mortgage life insurance
Lapse risk
Machine learning
External Data Sources
SDG 3 - Good health and well-being
SDG 4 - Quality education
SDG 9 - Industry, innovation and infrastructure
SDG 12 - Responsible production and consumption
SDG 13 - Climate action
Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação
title_short Understanding and predicting lapses in mortgage life insurance using a machine learning approach
title_full Understanding and predicting lapses in mortgage life insurance using a machine learning approach
title_fullStr Understanding and predicting lapses in mortgage life insurance using a machine learning approach
title_full_unstemmed Understanding and predicting lapses in mortgage life insurance using a machine learning approach
title_sort Understanding and predicting lapses in mortgage life insurance using a machine learning approach
author Manteigas, Carlos Manuel Andrade
author_facet Manteigas, Carlos Manuel Andrade
author_role author
dc.contributor.none.fl_str_mv António, Nuno Miguel da Conceição
RUN
dc.contributor.author.fl_str_mv Manteigas, Carlos Manuel Andrade
dc.subject.por.fl_str_mv Mortgage life insurance
Lapse risk
Machine learning
External Data Sources
SDG 3 - Good health and well-being
SDG 4 - Quality education
SDG 9 - Industry, innovation and infrastructure
SDG 12 - Responsible production and consumption
SDG 13 - Climate action
Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação
topic Mortgage life insurance
Lapse risk
Machine learning
External Data Sources
SDG 3 - Good health and well-being
SDG 4 - Quality education
SDG 9 - Industry, innovation and infrastructure
SDG 12 - Responsible production and consumption
SDG 13 - Climate action
Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação
description Project Work presented as the partial requirement for obtaining a Master's degree in Data Driven Marketing, specialization in Data Science for Marketing
publishDate 2024
dc.date.none.fl_str_mv 2024-03-14T15:27:04Z
2024-02-02
2024-02-02T00:00:00Z
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/10362/164918
TID:203544986
url http://hdl.handle.net/10362/164918
identifier_str_mv TID:203544986
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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