Customer Churn Prediction in Auto Insurance: Predicting policy cancelation for new clients

Detalhes bibliográficos
Autor(a) principal: Nunes, Ricardo de Sá
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/10362/160213
Resumo: Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data Science
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spelling Customer Churn Prediction in Auto Insurance: Predicting policy cancelation for new clientsCustomer ChurnInsuranceGeospatial variablesEnsembleGLMModel CalibrationDomínio/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 Data ScienceCustomer churn presents a challenge for any company, including insurers. Knowing which customers are more likely to cancel their insurance policy, enables the company to proactively take action to prevent such churn. To better describe the customers’ behavior and environment, new features can be created from external data. This project used data spanned from January 2019 to March 2023 from the auto branch of an insurance company. Given the availability of geospatial data, two new variables were added that help to portray the customer’s exposure to insurance intermediaries. Different feature selections and techniques to impute missing values were tested to build the probability model. After conducting a literature review on churn, four types of models were considered: random forests, neural networks, LightGBM, and XGBoost. To improve results, an ensemble was constructed using a Generalized Linear Model (GLM), and isotonic regression was applied to one of the models to calibrate the predictions. The main goal is to achieve a well-calibrated model whose probability predictions are expected to have the same percentage of confidence. To compare the models obtained, RMSE and LogLoss were used to measure the loss, while Expected Calibration Error (ECE) and reliability diagrams helped to assess the calibration.Castelli, MauroRUNNunes, Ricardo de Sá2023-10-272024-10-27T00:00:00Z2023-10-27T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/160213TID:203390369enginfo:eu-repo/semantics/embargoedAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:42:47Zoai:run.unl.pt:10362/160213Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:57:54.741514Repositório 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 Customer Churn Prediction in Auto Insurance: Predicting policy cancelation for new clients
title Customer Churn Prediction in Auto Insurance: Predicting policy cancelation for new clients
spellingShingle Customer Churn Prediction in Auto Insurance: Predicting policy cancelation for new clients
Nunes, Ricardo de Sá
Customer Churn
Insurance
Geospatial variables
Ensemble
GLM
Model Calibration
Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação
title_short Customer Churn Prediction in Auto Insurance: Predicting policy cancelation for new clients
title_full Customer Churn Prediction in Auto Insurance: Predicting policy cancelation for new clients
title_fullStr Customer Churn Prediction in Auto Insurance: Predicting policy cancelation for new clients
title_full_unstemmed Customer Churn Prediction in Auto Insurance: Predicting policy cancelation for new clients
title_sort Customer Churn Prediction in Auto Insurance: Predicting policy cancelation for new clients
author Nunes, Ricardo de Sá
author_facet Nunes, Ricardo de Sá
author_role author
dc.contributor.none.fl_str_mv Castelli, Mauro
RUN
dc.contributor.author.fl_str_mv Nunes, Ricardo de Sá
dc.subject.por.fl_str_mv Customer Churn
Insurance
Geospatial variables
Ensemble
GLM
Model Calibration
Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação
topic Customer Churn
Insurance
Geospatial variables
Ensemble
GLM
Model Calibration
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 Data Science
publishDate 2023
dc.date.none.fl_str_mv 2023-10-27
2023-10-27T00:00:00Z
2024-10-27T00:00:00Z
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/10362/160213
TID:203390369
url http://hdl.handle.net/10362/160213
identifier_str_mv TID:203390369
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/embargoedAccess
eu_rights_str_mv embargoedAccess
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
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