Overcoming over–indebtedness with AI - A case study on the application of AutoML to research

Detalhes bibliográficos
Autor(a) principal: Costa, Victor Cardoso Reis
Data de Publicação: 2021
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/115197
Resumo: Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics
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spelling Overcoming over–indebtedness with AI - A case study on the application of AutoML to researchover-indebtednesspoverty riskcredit controlartificial intelligencemachine learningautomated machine learningautomlDissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsThis research examines how artificial intelligence may contribute to better understanding and overcoming over-indebtedness in contexts of high poverty risk. This study uses a field database of 1,654 over-indebted households to identify distinguishable clusters and to predict its risk factors. First, unsupervised machine learning generated three overindebtedness clusters: low-income (31.27%), low credit control (37.40%), and crisis-affected households (31.33%). These served as basis for a better understanding on the complex issue that is over-indebtedness. Second, a predictive model was developed to serve as a tool for policymakers and advisory services by streamlining the classification of overindebtedness profiles. On building such model, an AutoML approach was leveraged achieving performant results (92.1% accuracy score). Furthermore, within the AutoML framework, two techniques were employed, leading to a deeper discussion on the benefits and inner workings of such strategy. Ultimately, this research looks to contribute on three fronts: theoretical, by unfolding previously unexplored characteristics on the concept of over-indebtedness; methodological, by proposing AutoML as a powerful research tool accessible to investigators on many backgrounds; and social, by building real-world applications that aim at mitigating over-indebtedness and, consequently, poverty risk.Castelli, MauroRUNCosta, Victor Cardoso Reis2021-04-08T10:56:13Z2021-03-302021-03-30T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/115197TID:202692469enginfo: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:RCAAP2024-03-11T04:57:50Zoai:run.unl.pt:10362/115197Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:42:42.502725Repositório 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 Overcoming over–indebtedness with AI - A case study on the application of AutoML to research
title Overcoming over–indebtedness with AI - A case study on the application of AutoML to research
spellingShingle Overcoming over–indebtedness with AI - A case study on the application of AutoML to research
Costa, Victor Cardoso Reis
over-indebtedness
poverty risk
credit control
artificial intelligence
machine learning
automated machine learning
automl
title_short Overcoming over–indebtedness with AI - A case study on the application of AutoML to research
title_full Overcoming over–indebtedness with AI - A case study on the application of AutoML to research
title_fullStr Overcoming over–indebtedness with AI - A case study on the application of AutoML to research
title_full_unstemmed Overcoming over–indebtedness with AI - A case study on the application of AutoML to research
title_sort Overcoming over–indebtedness with AI - A case study on the application of AutoML to research
author Costa, Victor Cardoso Reis
author_facet Costa, Victor Cardoso Reis
author_role author
dc.contributor.none.fl_str_mv Castelli, Mauro
RUN
dc.contributor.author.fl_str_mv Costa, Victor Cardoso Reis
dc.subject.por.fl_str_mv over-indebtedness
poverty risk
credit control
artificial intelligence
machine learning
automated machine learning
automl
topic over-indebtedness
poverty risk
credit control
artificial intelligence
machine learning
automated machine learning
automl
description Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics
publishDate 2021
dc.date.none.fl_str_mv 2021-04-08T10:56:13Z
2021-03-30
2021-03-30T00:00:00Z
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/10362/115197
TID:202692469
url http://hdl.handle.net/10362/115197
identifier_str_mv TID:202692469
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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