Personalized bank campaign using artificial neural networks
Autor(a) principal: | |
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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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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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1799137924554424320 |