Boto Ferreira Mário, Costa Pinto Diego, Maurer Herter Márcia, Soro Jerônimo, Vanneschi Leonardo, Castelli Mauro, Peres Fernando
Universidade de Lisboa, Faculdade de Psicologia, Portugal.
NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Portugal.
J Bus Res. 2021 Jul;131:411-425. doi: 10.1016/j.jbusres.2020.10.035. Epub 2020 Oct 19.
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.
本研究探讨了人工智能如何有助于在高贫困风险背景下更好地理解和克服过度负债问题。本研究在一个包含1654个过度负债家庭的实地数据库中使用自动机器学习（AutoML）来识别可区分的集群，并预测其风险因素。首先，使用自组织映射的无监督机器学习生成了三个过度负债集群：低收入家庭（31.27%）、低信贷控制家庭（37.40%）和受危机影响家庭（31.33%）。其次，采用具有详尽网格搜索超参数的监督机器学习（32730个预测模型）表明，Nu-支持向量机在预测家庭过度负债风险因素方面具有最佳准确率（89.5%）。通过提出一种关于过度负债的自动机器学习方法，我们的研究在理论和方法上对当前的稀缺模型进行了补充，对商业研究和社会具有重要的实际意义。我们的研究结果还为在早期阶段识别和描述贫困风险提供了新方法，从而能够针对不同的过度负债情况进行定制化干预。
