Using artificial intelligence to overcome over-indebtedness and fight poverty

Detalhes bibliográficos
Autor(a) principal: Boto Ferreira, Mário
Data de Publicação: 2021
Outros Autores: Costa Pinto, Diego, Maurer Herter, Márcia, Soro, Jerônimo, Vanneschi, Leonardo, Castelli, Mauro, Peres, Fernando
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/106280
Resumo: Boto Ferreira, M., Costa Pinto, D., Maurer Herter, M., Soro, J., Vanneschi, L., Castelli, M., & Peres, F. (2021). Using artificial intelligence to overcome over-indebtedness and fight poverty. Journal of Business Research, 131, 411-425. [Advanced online publication on 19 October 2020]. https://doi.org/10.1016/j.jbusres.2020.10.035 --- This study was partially supported by Grant PTDC/MHC­PAP/1556/2014 and DSAIPA/DS/0113/2019 from the Foundation for Science and Technology of the Ministry of Science and Higher Education (Portugal).
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spelling Using artificial intelligence to overcome over-indebtedness and fight povertyArtificial intelligenceAutomated machine learningCredit controlEconomic austerityOver-indebtednessPoverty riskMarketingSDG 1 - No PovertyBoto Ferreira, M., Costa Pinto, D., Maurer Herter, M., Soro, J., Vanneschi, L., Castelli, M., & Peres, F. (2021). Using artificial intelligence to overcome over-indebtedness and fight poverty. Journal of Business Research, 131, 411-425. [Advanced online publication on 19 October 2020]. https://doi.org/10.1016/j.jbusres.2020.10.035 --- This study was partially supported by Grant PTDC/MHC­PAP/1556/2014 and DSAIPA/DS/0113/2019 from the Foundation for Science and Technology of the Ministry of Science and Higher Education (Portugal).This research examines how artificial intelligence may contribute to better understanding and to overcome over-indebtedness in contexts of high poverty risk. This research uses Automated Machine Learning (AutoML) in a field database of 1654 over-indebted households to identify distinguishable clusters and to predict its risk factors. First, unsupervised machine learning using Self-Organizing Maps generated three over-indebtedness clusters: low-income (31.27%), low credit control (37.40%), and crisis-affected households (31.33%). Second, supervised machine learning with exhaustive grid search hyperparameters (32,730 predictive models) suggests that Nu-Support Vector Machine had the best accuracy in predicting families’ over-indebtedness risk factors (89.5%). By proposing an AutoML approach on over-indebtedness, our research adds both theoretically and methodologically to current models of scarcity with important practical implications for business research and society. Our findings also contribute to novel ways to identify and characterize poverty risk in earlier stages, allowing customized interventions for different profiles of over-indebtedness.NOVA Information Management School (NOVA IMS)Information Management Research Center (MagIC) - NOVA Information Management SchoolRUNBoto Ferreira, MárioCosta Pinto, DiegoMaurer Herter, MárciaSoro, JerônimoVanneschi, LeonardoCastelli, MauroPeres, Fernando2024-02-08T01:31:31Z2021-07-012021-07-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article15application/pdfhttp://hdl.handle.net/10362/106280eng0148-2963PURE: 26084877https://doi.org/10.1016/j.jbusres.2020.10.035info: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:RCAAP2024-03-11T04:51:20Zoai:run.unl.pt:10362/106280Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:40:41.273189Repositório 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 Using artificial intelligence to overcome over-indebtedness and fight poverty
title Using artificial intelligence to overcome over-indebtedness and fight poverty
spellingShingle Using artificial intelligence to overcome over-indebtedness and fight poverty
Boto Ferreira, Mário
Artificial intelligence
Automated machine learning
Credit control
Economic austerity
Over-indebtedness
Poverty risk
Marketing
SDG 1 - No Poverty
title_short Using artificial intelligence to overcome over-indebtedness and fight poverty
title_full Using artificial intelligence to overcome over-indebtedness and fight poverty
title_fullStr Using artificial intelligence to overcome over-indebtedness and fight poverty
title_full_unstemmed Using artificial intelligence to overcome over-indebtedness and fight poverty
title_sort Using artificial intelligence to overcome over-indebtedness and fight poverty
author Boto Ferreira, Mário
author_facet Boto Ferreira, Mário
Costa Pinto, Diego
Maurer Herter, Márcia
Soro, Jerônimo
Vanneschi, Leonardo
Castelli, Mauro
Peres, Fernando
author_role author
author2 Costa Pinto, Diego
Maurer Herter, Márcia
Soro, Jerônimo
Vanneschi, Leonardo
Castelli, Mauro
Peres, Fernando
author2_role author
author
author
author
author
author
dc.contributor.none.fl_str_mv NOVA Information Management School (NOVA IMS)
Information Management Research Center (MagIC) - NOVA Information Management School
RUN
dc.contributor.author.fl_str_mv Boto Ferreira, Mário
Costa Pinto, Diego
Maurer Herter, Márcia
Soro, Jerônimo
Vanneschi, Leonardo
Castelli, Mauro
Peres, Fernando
dc.subject.por.fl_str_mv Artificial intelligence
Automated machine learning
Credit control
Economic austerity
Over-indebtedness
Poverty risk
Marketing
SDG 1 - No Poverty
topic Artificial intelligence
Automated machine learning
Credit control
Economic austerity
Over-indebtedness
Poverty risk
Marketing
SDG 1 - No Poverty
description Boto Ferreira, M., Costa Pinto, D., Maurer Herter, M., Soro, J., Vanneschi, L., Castelli, M., & Peres, F. (2021). Using artificial intelligence to overcome over-indebtedness and fight poverty. Journal of Business Research, 131, 411-425. [Advanced online publication on 19 October 2020]. https://doi.org/10.1016/j.jbusres.2020.10.035 --- This study was partially supported by Grant PTDC/MHC­PAP/1556/2014 and DSAIPA/DS/0113/2019 from the Foundation for Science and Technology of the Ministry of Science and Higher Education (Portugal).
publishDate 2021
dc.date.none.fl_str_mv 2021-07-01
2021-07-01T00:00:00Z
2024-02-08T01:31:31Z
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/10362/106280
url http://hdl.handle.net/10362/106280
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 0148-2963
PURE: 26084877
https://doi.org/10.1016/j.jbusres.2020.10.035
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
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application/pdf
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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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