Personalized bank campaign using artificial neural networks

Detalhes bibliográficos
Autor(a) principal: Simota, Asimina
Data de Publicação: 2018
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/33280
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 Personalized bank campaign using artificial neural networksBankCross-sell and up-sellProbabilitiesArtificial neural networksPerformanceInternship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsNowadays, high market competition requires Banks to focus more at individual customers´ behaviors. Specifically, customers prefer a personal relationship with the finance institution and they want to receive exclusive offers. Thus, a successful cross-sell and up- sell personalized campaign requires to know the individual client interest for the offer. The aim of this project is to create a model, that, is able to identify the probability of a customer to buy a product of the bank. The strategic plan is to run a long-term personalized campaign and the challenge is to create a model which remains accurate during this time. The source datasets consist of 12 dataMarts, which represent a monthly snapshot of the Bank’s dataWarehouse between April 2016 and March 2017. They consist of 191 original variables, which contain personal and transactional information and around 1.400.000 clients each. The selected modeling technique is Artificial Neural Networks and specifically, Multilayer Perceptron running with Back-propagation. The results showed that the model performs well and the business can use it to optimize the profitability. Despite the good results, business must monitor the model´s outputs to check the performance through time.Vanneschi, LeonardoRUNSimota, Asimina2018-03-26T11:02:45Z2018-02-152018-02-15T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/33280TID:201865645enginfo: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:RCAAP2024-03-11T04:18:22Zoai:run.unl.pt:10362/33280Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:30:00.943483Repositório 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 Personalized bank campaign using artificial neural networks
title Personalized bank campaign using artificial neural networks
spellingShingle Personalized bank campaign using artificial neural networks
Simota, Asimina
Bank
Cross-sell and up-sell
Probabilities
Artificial neural networks
Performance
title_short Personalized bank campaign using artificial neural networks
title_full Personalized bank campaign using artificial neural networks
title_fullStr Personalized bank campaign using artificial neural networks
title_full_unstemmed Personalized bank campaign using artificial neural networks
title_sort Personalized bank campaign using artificial neural networks
author Simota, Asimina
author_facet Simota, Asimina
author_role author
dc.contributor.none.fl_str_mv Vanneschi, Leonardo
RUN
dc.contributor.author.fl_str_mv Simota, Asimina
dc.subject.por.fl_str_mv Bank
Cross-sell and up-sell
Probabilities
Artificial neural networks
Performance
topic Bank
Cross-sell and up-sell
Probabilities
Artificial neural networks
Performance
description Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics
publishDate 2018
dc.date.none.fl_str_mv 2018-03-26T11:02:45Z
2018-02-15
2018-02-15T00:00:00Z
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/10362/33280
TID:201865645
url http://hdl.handle.net/10362/33280
identifier_str_mv TID:201865645
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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