Gauging and foreseeing customer churn in the banking industry : a neural network approach

Detalhes bibliográficos
Autor(a) principal: Rosa, Nelson Belém da Costa
Data de Publicação: 2019
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/71584
Resumo: Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business Intelligence
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spelling Gauging and foreseeing customer churn in the banking industry : a neural network approachBankingBusiness IntelligenceCustomer ChurnData MiningNeural NetworksProject Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceIn today´s highly competitive market where information is easily accessible and with ever decreasing switching costs, customers in any industry hardly hesitate to move their business elsewhere should they find more suitable proposals to accommodate their needs. As such, it is in the best interest of any company to keep a close watch on its customers to monitor any signs of potential churning down the line. Significant headways in the Business Intelligence field have brought forward a great number of tools for knowledge discovery and predictive analytics purposes. This paper proposes a new framework for assessing and predicting customer attrition in one of the biggest Portuguese retail banks. Data mining techniques were employed to study the behavior and patterns of past churners. A set of predictor variables was obtained from this process and used to train a set of predictive models using neural networks. Assessment of the performance of the classifiers was later validated on a sample of current customers in risk of churning. The goal of this project is to provide a new approach to identify potential churners so marketing retention strategies be developed accordingly.Henriques, Roberto André PereiraRUNRosa, Nelson Belém da Costa2019-06-03T13:56:25Z2019-04-092019-04-09T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/71584TID:202250709enginfo: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:RCAAP2024-03-11T04:33:40Zoai:run.unl.pt:10362/71584Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:35:12.764550Repositório 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 Gauging and foreseeing customer churn in the banking industry : a neural network approach
title Gauging and foreseeing customer churn in the banking industry : a neural network approach
spellingShingle Gauging and foreseeing customer churn in the banking industry : a neural network approach
Rosa, Nelson Belém da Costa
Banking
Business Intelligence
Customer Churn
Data Mining
Neural Networks
title_short Gauging and foreseeing customer churn in the banking industry : a neural network approach
title_full Gauging and foreseeing customer churn in the banking industry : a neural network approach
title_fullStr Gauging and foreseeing customer churn in the banking industry : a neural network approach
title_full_unstemmed Gauging and foreseeing customer churn in the banking industry : a neural network approach
title_sort Gauging and foreseeing customer churn in the banking industry : a neural network approach
author Rosa, Nelson Belém da Costa
author_facet Rosa, Nelson Belém da Costa
author_role author
dc.contributor.none.fl_str_mv Henriques, Roberto André Pereira
RUN
dc.contributor.author.fl_str_mv Rosa, Nelson Belém da Costa
dc.subject.por.fl_str_mv Banking
Business Intelligence
Customer Churn
Data Mining
Neural Networks
topic Banking
Business Intelligence
Customer Churn
Data Mining
Neural Networks
description Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business Intelligence
publishDate 2019
dc.date.none.fl_str_mv 2019-06-03T13:56:25Z
2019-04-09
2019-04-09T00:00:00Z
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/10362/71584
TID:202250709
url http://hdl.handle.net/10362/71584
identifier_str_mv TID:202250709
dc.language.iso.fl_str_mv eng
language eng
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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
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