Automated Machine Learning implementation framework in the banking sector
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Tipo de documento: | Dissertação |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10362/134199 |
Resumo: | Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business Analytics |
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Automated Machine Learning implementation framework in the banking sectorMachine LearningArtificial IntelligenceData ScienceAdvanced AnalyticsAutomated Machine LearningBankingDissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business AnalyticsAutomated Machine Learning is a subject in the Machine Learning field, designed to give the possibility of Machine Learning use to non-expert users, it aroused from the lack of subject matter experts, trying to remove humans from these topic implementations. The advantages behind automated machine learning are leaning towards the removal of human implementation, fastening the machine learning deployment speed. The organizations will benefit from effective solutions benchmarking and validations. The use of an automated machine learning implementation framework can deeply transform an organization adding value to the business by freeing the subject matter experts of the low-level machine learning projects, letting them focus on high level projects. This will also help the organization reach new competence, customization, and decision-making levels in a higher analytical maturity level. This work pretends, firstly to investigate the impact and benefits automated machine learning implementation in the banking sector, and afterwards develop an implementation framework that could be used by banking institutions as a guideline for the automated machine learning implementation through their departments. The autoML advantages and benefits are evaluated regarding business value and competitive advantage and it is presented the implementation in a fictitious institution, considering all the need steps and the possible setbacks that could arise. Banking institutions, in their business have different business processes, and since most of them are old institutions, the main concerns are related with the automating their business process, improving their analytical maturity and sensibilizing their workforce to the benefits of the implementation of new forms of work. To proceed to a successful implementation plan should be known the institution particularities, adapt to them and ensured the sensibilization of the workforce and management to the investments that need to be made and the changes in all levels of their organizational work that will come from that, that will lead to a lot of facilities in everyone’s daily work.Santos, Vitor Manuel Pereira Duarte dosRUNCarmona, Pedro Bernardo Resina Baptista Barreiros2022-03-10T11:38:52Z2022-01-242022-01-24T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/134199TID:202960048enginfo: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:RCAAP2024-03-11T05:12:39Zoai:run.unl.pt:10362/134199Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:48:01.041591Repositório 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 |
Automated Machine Learning implementation framework in the banking sector |
title |
Automated Machine Learning implementation framework in the banking sector |
spellingShingle |
Automated Machine Learning implementation framework in the banking sector Carmona, Pedro Bernardo Resina Baptista Barreiros Machine Learning Artificial Intelligence Data Science Advanced Analytics Automated Machine Learning Banking |
title_short |
Automated Machine Learning implementation framework in the banking sector |
title_full |
Automated Machine Learning implementation framework in the banking sector |
title_fullStr |
Automated Machine Learning implementation framework in the banking sector |
title_full_unstemmed |
Automated Machine Learning implementation framework in the banking sector |
title_sort |
Automated Machine Learning implementation framework in the banking sector |
author |
Carmona, Pedro Bernardo Resina Baptista Barreiros |
author_facet |
Carmona, Pedro Bernardo Resina Baptista Barreiros |
author_role |
author |
dc.contributor.none.fl_str_mv |
Santos, Vitor Manuel Pereira Duarte dos RUN |
dc.contributor.author.fl_str_mv |
Carmona, Pedro Bernardo Resina Baptista Barreiros |
dc.subject.por.fl_str_mv |
Machine Learning Artificial Intelligence Data Science Advanced Analytics Automated Machine Learning Banking |
topic |
Machine Learning Artificial Intelligence Data Science Advanced Analytics Automated Machine Learning Banking |
description |
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business Analytics |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-03-10T11:38:52Z 2022-01-24 2022-01-24T00:00:00Z |
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/10362/134199 TID:202960048 |
url |
http://hdl.handle.net/10362/134199 |
identifier_str_mv |
TID:202960048 |
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 |
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1799138081625866240 |