Machine learning algorithms to predict the use of digital financial platforms in university environments

The advancement of artificial intelligence and machine learning has transformed the financial system. The purpose of this article is to analyze the predictive capacity of machine learning algorithms regarding the factors that influence the use of digital financial platforms in university environments. A quantitative and applied approach was adopted, with a non-experimental and cross-sectional design. From the population of users of digital financial platforms at the Universidad Estatal de Milagro, a sample of 968 valid records was obtained, of which 83.16% were used for model training and 16.84% for validation. Nine supervised algorithms were applied, including Random Forest, Gradient Boosting, Logistic Regression, and Artificial Neural Networks. The results showed a high predictive capacity in decision tree based models, highlighting the significant influence of financial literacy, educational level, age, and frequency of use on the adoption of digital financial platforms. A positive correlation was confirmed between financial knowledge and the efficient use of digital tools. The study concludes that machine learning algorithms are effective tools for predicting patterns of digital financial use and for optimizing decision making in academic settings.