Harnessing Data Science for Debt Reduction: Insights from Structural Equation Modeling.

Abstract

High levels of consumer indebtedness have been a concern in recent years, as they have contributed to rising household indebtedness and put pressure on the country’s financial stability. The general objective of this study was to present actions to reduce the individual’s indebtedness. To achieve this objective, exploratory and explanatory research was carried out. First, there was a review of the literature to find variables and/or models that explain indebtedness, and in a second part, with a quantitative character via structural equations, a questionnaire was applied to a sample of 114 individuals from the Federal District. The variables that most influenced were financial literacy (21.1%), materialism (5.45), and risk perception (3.4%). Among the actions to improve indebtedness are to make the individual literate, explain the risks associated with short-term investments and promises of immediate gain, and the consequences of materialism, which can help minimize indebtedness.