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.
Keywords: Artificial intelligence; Automated machine learning; Credit control; Economic austerity; Over-indebtedness; Poverty risk.
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