Modeling Income Declaration Behavior in Chilean Student Loans: A Dataset and Baseline Predictive Analysis of the FSCU Program

This work presents a novel dataset designed to support predictive modeling of income declaration behavior in the University Credit Solidarity Fund (FSCU) program of Chile. Using institutional records from the Pontificia Universidad Católica de Valparaíso, we prepared a structured and anonymized dataset that captures academic, financial, and demographic variables relevant to student loan administration. We describe the data extraction and preprocessing pipeline, including entity modeling, feature engineering, and the creation of a relational database. As a baseline evaluation, we conducted initial experiments using 15 machine learning algorithms via PyCaret, aiming to predict whether a student who previously submitted income declarations would cease to do so. Although no hyperparameter tuning or data balancing was applied, the results offer a solid foundation for future experimentation. Boosting-based classifiers-particularly XGBoost-achieved the best performance, with accuracy and AUC exceeding 96%. This contribution addresses the lack of publicly available, context-sensitive datasets for modeling conditional repayment schemes in higher education finance. Future work includes dataset publication, class balancing, and explainability analysis.