Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsThis research examines how artificial intelligence may contribute to better understanding
and overcoming over-indebtedness in contexts of high poverty risk. This study uses a field
database of 1,654 over-indebted households to identify distinguishable clusters and to
predict its risk factors. First, unsupervised machine learning generated three overindebtedness
clusters: low-income (31.27%), low credit control (37.40%), and crisis-affected
households (31.33%). These served as basis for a better understanding on the complex
issue that is over-indebtedness. Second, a predictive model was developed to serve as a
tool for policymakers and advisory services by streamlining the classification of overindebtedness
profiles. On building such model, an AutoML approach was leveraged
achieving performant results (92.1% accuracy score). Furthermore, within the AutoML
framework, two techniques were employed, leading to a deeper discussion on the benefits
and inner workings of such strategy. Ultimately, this research looks to contribute on three
fronts: theoretical, by unfolding previously unexplored characteristics on the concept of
over-indebtedness; methodological, by proposing AutoML as a powerful research tool
accessible to investigators on many backgrounds; and social, by building real-world
applications that aim at mitigating over-indebtedness and, consequently, poverty risk
