Customer Churn Prediction in Insurance: Modeling Renewal Price Elasticity of the Workers’ Compensation Portfolio from Ocidental Seguros

Detalhes bibliográficos
Autor(a) principal: Castro, Laura Sofia Sauthoff da Ponte e
Data de Publicação: 2022
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/141571
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 Customer Churn Prediction in Insurance: Modeling Renewal Price Elasticity of the Workers’ Compensation Portfolio from Ocidental SegurosSupervised LearningClassificationCustomer Churn PredictionNon-Life InsuranceRenewal Price ElasticityClusteringNeural NetworkLogistic RegressionStochastic Gradient BoostingInternship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsCustomer churn has been increasing in insurance, mainly due to technological improvements that allow customers to explore other insurance providers’ offers. Given this, insurance providers need to compete among them, not only to get new customers but, to maintain their own. This report results from a project developed during an internship in Grupo Ageas Portugal, which has different insurance brands such as Ocidental Seguros. This project's main goal was to model, with a monthly periodicity, customer churn of this latter’s Workers’ Compensation portfolio to improve the company’s competitiveness and, ultimately, profit. Many of the company’s customer churn happens at their policy renewal time, where the only variable that the company detains control over is the price (premium) variation. Hence, by considering the premium variation and other relevant predictive variables, the goal was to predict the probability of a given customer to churn, allowing the company to optimize the current renewal’s pricing process and maximize this branch’s profit. Thus, different variables that could influence the company’s customer behavior were collected—one of those was the customer’s location. Given the high dimensionality that such variable would represent and the small dataset available for modeling, clustering analysis is used to create new significant (with fewer dimensions) customer geographical areas. Different supervised learning algorithms were then evaluated accordingly to their performance in predicting customer churn. The predictive models used were a Gradient Boosting, an Extreme Gradient Boosting, a Logistic Regression, and a Multilayer Perceptron. Given that the number of customers that renew their contracts is much superior to the number of customers who churn, Synthetic Minority Oversampling Technique (SMOTE) was used to create less unbalanced datasets (with synthetic samples) and evaluate the impact on the performance of one of the models. Lastly, to guarantee a successful integration of the models into the renewal’s pricing process, models were evaluated accordingly to the two business goals. First, by translating the observed evaluation metrics into profit. Secondly, by assuring that the customer’s price elasticity would be captured, assuring a monotonic increasing relationship among the policy’s premium variation and probability of churn.Mendes, Jorge MoraisAntónio, Nuno Miguel da ConceiçãoRUNCastro, Laura Sofia Sauthoff da Ponte e2022-07-08T15:27:10Z2022-05-122022-05-12T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/141571TID:203035151enginfo: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:RCAAP2024-03-11T05:18:59Zoai:run.unl.pt:10362/141571Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:50:02.378969Repositório 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 Insurance: Modeling Renewal Price Elasticity of the Workers’ Compensation Portfolio from Ocidental Seguros
title Customer Churn Prediction in Insurance: Modeling Renewal Price Elasticity of the Workers’ Compensation Portfolio from Ocidental Seguros
spellingShingle Customer Churn Prediction in Insurance: Modeling Renewal Price Elasticity of the Workers’ Compensation Portfolio from Ocidental Seguros
Castro, Laura Sofia Sauthoff da Ponte e
Supervised Learning
Classification
Customer Churn Prediction
Non-Life Insurance
Renewal Price Elasticity
Clustering
Neural Network
Logistic Regression
Stochastic Gradient Boosting
title_short Customer Churn Prediction in Insurance: Modeling Renewal Price Elasticity of the Workers’ Compensation Portfolio from Ocidental Seguros
title_full Customer Churn Prediction in Insurance: Modeling Renewal Price Elasticity of the Workers’ Compensation Portfolio from Ocidental Seguros
title_fullStr Customer Churn Prediction in Insurance: Modeling Renewal Price Elasticity of the Workers’ Compensation Portfolio from Ocidental Seguros
title_full_unstemmed Customer Churn Prediction in Insurance: Modeling Renewal Price Elasticity of the Workers’ Compensation Portfolio from Ocidental Seguros
title_sort Customer Churn Prediction in Insurance: Modeling Renewal Price Elasticity of the Workers’ Compensation Portfolio from Ocidental Seguros
author Castro, Laura Sofia Sauthoff da Ponte e
author_facet Castro, Laura Sofia Sauthoff da Ponte e
author_role author
dc.contributor.none.fl_str_mv Mendes, Jorge Morais
António, Nuno Miguel da Conceição
RUN
dc.contributor.author.fl_str_mv Castro, Laura Sofia Sauthoff da Ponte e
dc.subject.por.fl_str_mv Supervised Learning
Classification
Customer Churn Prediction
Non-Life Insurance
Renewal Price Elasticity
Clustering
Neural Network
Logistic Regression
Stochastic Gradient Boosting
topic Supervised Learning
Classification
Customer Churn Prediction
Non-Life Insurance
Renewal Price Elasticity
Clustering
Neural Network
Logistic Regression
Stochastic Gradient Boosting
description Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics
publishDate 2022
dc.date.none.fl_str_mv 2022-07-08T15:27:10Z
2022-05-12
2022-05-12T00:00:00Z
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/10362/141571
TID:203035151
url http://hdl.handle.net/10362/141571
identifier_str_mv TID:203035151
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
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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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