Publicação
Using artificial intelligence to overcome over-indebtedness and fight poverty
| Resumo: | This research examines how artifcial intelligence may contribute to better understanding and to overcome overindebtedness in contexts of high poverty risk. This research uses Automated Machine Learning (AutoML) in a feld 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 NuSupport 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 fndings also contribute to novel ways to identify and characterize poverty risk in earlier stages, allowing customized interventions for different profles of over-indebtedness. |
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| Autores principais: | Ferreira, Mário B. |
| Outros Autores: | Pinto, Diego; Herter, M. M.; C. Soro, Jerônimo; Vanneschi, Leonardo; Castelli, Mauro; Peres, Fernando |
| Assunto: | Over-indebtedness Poverty risk Economic austerity Credit control Artificial intelligence Automated machine learning |
| Ano: | 2020 |
| País: | Portugal |
| Tipo de documento: | artigo |
| Tipo de acesso: | acesso restrito |
| Instituição associada: | Universidade de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório da Universidade de Lisboa |
| Resumo: | This research examines how artifcial intelligence may contribute to better understanding and to overcome overindebtedness in contexts of high poverty risk. This research uses Automated Machine Learning (AutoML) in a feld 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 NuSupport 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 fndings also contribute to novel ways to identify and characterize poverty risk in earlier stages, allowing customized interventions for different profles of over-indebtedness. |
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