A machine learning framework towards bank telemarketing prediction

Detalhes bibliográficos
Autor(a) principal: KOUMETIO TEKOUABOU, Stéphane Cédric
Data de Publicação: 2022
Outros Autores: Gherghina, Ştefan Cristian, TOULNI, Hamza, Mata, Pedro, Mata, Mário Nuno, Moleiro Martins, José
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10400.21/14762
Resumo: Artigo publicado em revista científica internacional
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spelling A machine learning framework towards bank telemarketing predictionArtificial intelligenceData miningHeterogeneous dataMachine learningPerformance optimisationPredictive modellingTargeted marketingBank telemarketingArtigo publicado em revista científica internacionalThe use of machine learning (ML) methods has been widely discussed for over a decade. The search for the optimal model is still a challenge that researchers seek to address. Despite advances in current work that surpass the limitations of previous ones, research still faces new challenges in every field. For the automatic targeting of customers in a banking telemarketing campaign, the use of ML-based approaches in previous work has not been able to show transparency in the processing of heterogeneous data, achieve optimal performance or use minimal resources. In this paper, we introduce a class membership-based (CMB) classifier which is a transparent approach well adapted to heterogeneous data that exploits nominal variables in the decision function. These dummy variables are often either suppressed or coded in an arbitrary way in most works without really evaluating their impact on the final performance of the models. In many cases, their coding either favours or disfavours the learning model performance without necessarily reflecting reality, which leads to over-fitting or decreased performance. In this work, we applied the CMB approach to data from a bank telemarketing campaign to build an optimal model for predicting potential customers before launching a campaign. The results obtained suggest that the CMB approach can predict the success of future prospecting more accurately than previous work. Furthermore, in addition to its better performance in terms of accuracy (97.3%), the model also gives a very close score for the AUC (95.9%), showing its stability, which would be very unfavourable to over-fitting.MDPIRCIPLKOUMETIO TEKOUABOU, Stéphane CédricGherghina, Ştefan CristianTOULNI, HamzaMata, PedroMata, Mário NunoMoleiro Martins, José2022-06-29T11:07:28Z2022-062022-06-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.21/14762engTékouabou, S. C. K., Gherghina, Ş. C., Toulni, H., Neves Mata, P., Mata, M. N., & Martins, J. M. (2022). A Machine Learning Framework towards Bank Telemarketing Prediction. Journal of Risk and Financial Management, 15(6), 269. https://doi.org/10.3390/jrfm15060269https://doi.org/10.3390/jrfm15060269info: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:RCAAP2023-08-03T10:11:22Zoai:repositorio.ipl.pt:10400.21/14762Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T20:22:30.751304Repositório 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 A machine learning framework towards bank telemarketing prediction
title A machine learning framework towards bank telemarketing prediction
spellingShingle A machine learning framework towards bank telemarketing prediction
KOUMETIO TEKOUABOU, Stéphane Cédric
Artificial intelligence
Data mining
Heterogeneous data
Machine learning
Performance optimisation
Predictive modelling
Targeted marketing
Bank telemarketing
title_short A machine learning framework towards bank telemarketing prediction
title_full A machine learning framework towards bank telemarketing prediction
title_fullStr A machine learning framework towards bank telemarketing prediction
title_full_unstemmed A machine learning framework towards bank telemarketing prediction
title_sort A machine learning framework towards bank telemarketing prediction
author KOUMETIO TEKOUABOU, Stéphane Cédric
author_facet KOUMETIO TEKOUABOU, Stéphane Cédric
Gherghina, Ştefan Cristian
TOULNI, Hamza
Mata, Pedro
Mata, Mário Nuno
Moleiro Martins, José
author_role author
author2 Gherghina, Ştefan Cristian
TOULNI, Hamza
Mata, Pedro
Mata, Mário Nuno
Moleiro Martins, José
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv RCIPL
dc.contributor.author.fl_str_mv KOUMETIO TEKOUABOU, Stéphane Cédric
Gherghina, Ştefan Cristian
TOULNI, Hamza
Mata, Pedro
Mata, Mário Nuno
Moleiro Martins, José
dc.subject.por.fl_str_mv Artificial intelligence
Data mining
Heterogeneous data
Machine learning
Performance optimisation
Predictive modelling
Targeted marketing
Bank telemarketing
topic Artificial intelligence
Data mining
Heterogeneous data
Machine learning
Performance optimisation
Predictive modelling
Targeted marketing
Bank telemarketing
description Artigo publicado em revista científica internacional
publishDate 2022
dc.date.none.fl_str_mv 2022-06-29T11:07:28Z
2022-06
2022-06-01T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10400.21/14762
url http://hdl.handle.net/10400.21/14762
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Tékouabou, S. C. K., Gherghina, Ş. C., Toulni, H., Neves Mata, P., Mata, M. N., & Martins, J. M. (2022). A Machine Learning Framework towards Bank Telemarketing Prediction. Journal of Risk and Financial Management, 15(6), 269. https://doi.org/10.3390/jrfm15060269
https://doi.org/10.3390/jrfm15060269
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.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
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
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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
repository.mail.fl_str_mv
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