A Unified Pipeline for Improving Financial Aid Eligibility Predictions in Deep Learning Models

Abstract

This paper systematically evaluates the performance of widely used deep learning architectures for financial aid prediction, identifying critical bottlenecks that hinder optimal performance. To address these limitations, we further propose a novel pipeline that enhances deep learning models by incorporating three key components: text vectorization, data equalization, and adaptive feature recalibration. This pipeline improves the models’ representational power and predictive accuracy, offering seamless integration with existing architectures. It significantly boosts performance in predicting financial aid eligibility, providing up to a 145% increase in balanced accuracy and a 45% increase in F1 score.