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A machine learning framework towards bank telemarketing prediction

2022-06Journal article Open access
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dc.contributor.authorKOUMETIO TEKOUABOU, Stéphane Cédric
dc.contributor.authorGherghina, Ştefan Cristian
dc.contributor.authorTOULNI, Hamza
dc.contributor.authorMata, Pedro
dc.contributor.authorMata, Mário Nuno
dc.contributor.authorMoleiro Martins, José
dc.date.accessioned2022-06-29T11:07:28Z
dc.date.available2022-06-29T11:07:28Z
dc.date.issued2022-06
dc.descriptionArtigo publicado em revista científica internacionalpt_PT
dc.description.abstractThe 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.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationTékouabou, 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/jrfm15060269pt_PT
dc.identifier.doihttps://doi.org/10.3390/jrfm15060269pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.21/14762
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherMDPIpt_PT
dc.relation.ispartofseries;6
dc.relation.publisherversionhttps://www.mdpi.com/1911-8074/15/6/269pt_PT
dc.subjectArtificial intelligencept_PT
dc.subjectData miningpt_PT
dc.subjectHeterogeneous datapt_PT
dc.subjectMachine learningpt_PT
dc.subjectPerformance optimisationpt_PT
dc.subjectPredictive modellingpt_PT
dc.subjectTargeted marketingpt_PT
dc.subjectBank telemarketingpt_PT
dc.titleA machine learning framework towards bank telemarketing predictionpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage19pt_PT
oaire.citation.startPage1pt_PT
oaire.citation.titleJournal of Risk and Financial Managementpt_PT
oaire.citation.volume15pt_PT
person.familyNameKOUMETIO TEKOUABOU
person.familyNameGherghina
person.familyNameTOULNI
person.familyNameMata
person.familyNameMata
person.familyNameMoleiro Martins
person.givenNameStéphane Cédric
person.givenNameŞtefan Cristian
person.givenNameHamza
person.givenNamePedro
person.givenNameMário Nuno
person.givenNameJosé
person.identifier987275
person.identifier1403614
person.identifier1485846
person.identifier.ciencia-idFA13-1761-4192
person.identifier.ciencia-id5F1F-3C6C-A7BD
person.identifier.orcid0000-0003-3627-5746
person.identifier.orcid0000-0003-2911-6480
person.identifier.orcid0000-0002-6598-6267
person.identifier.orcid0000-0001-8465-9539
person.identifier.orcid0000-0003-1765-4273
person.identifier.orcid0000-0001-6853-2917
person.identifier.ridJ-3339-2012
person.identifier.scopus-author-id57215085805
person.identifier.scopus-author-id56046530600
person.identifier.scopus-author-id55842206000
person.identifier.scopus-author-id36008956400
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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