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Financial literacy in Ecuadorian university students: An application of Machine Learning

Abstract:

The use of machine learning techniques has gained prominence in various research areas, including economics and finance. In this research, we apply these techniques to the field of financial education to assess their accuracy compared to traditional econometric approaches. Using data from a survey of university students in Ecuador, we examine how machine learning methods such as logistic regression, adaboost, random forest, nearest neighbors, neural networks, and support vector machines are effective in predicting both financial literacy and its determinants in a particularly exposed population segment. Our results not only demonstrate the effectiveness of these methods in estimating financial literacy, but also highlight their usefulness in better understanding the key determinants of financial literacy in university students.