Using artificial intelligence to overcome over-indebtedness and fight poverty
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , , , , |
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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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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15 application/pdf |
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reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799138020997201920 |