# Bioinspired Deep Neural Networks for Predicting Income-Reporting Discontinuities in the Chilean Student Loan Program

## TL;DR

This paper introduces a bioinspired deep neural network to predict income reporting discontinuities in Chile's student loan program, outperforming traditional models.

## Contribution

A novel bioinspired DNN is proposed and shown to significantly outperform classical models in predicting income discontinuities.

## Key findings

- The bioinspired DNN outperformed Random Forest with higher AUC and F1-score metrics.
- Financial variables were found to be the most influential in predictions.
- The DNN showed lower variability across multiple data partitions.

## Abstract

This study addresses discontinuity prediction in income reporting within the Chilean student loan program, a critical event for credit risk management. Although the literature has incorporated machine learning models to anticipate non-compliance behavior, a gap remains in the development of methodologically robust evaluations that integrate nonlinear imputation, imbalance correction, and repeated validation across multiple partitions. To address this need, a complete pipeline was implemented on a dataset of 22,303 records, including MissForest imputation, SMOTE-based balancing, and a comparative assessment of a biologically inspired Deep Neural Network (DNN) and a Random Forest (RF) classifier used as a classical baseline model, evaluated across 35 stratified partitions. The results show that the bioinspired DNN, as the primary focus of this study, consistently outperforms the RF in metrics such as AUC (0.9991 vs 0.9709), F1-score (0.9966 vs 0.9497), and agreement measures, while also exhibiting lower variability across partitions. The interpretability analysis indicates that financial variables account for the greatest influence on predictions, whereas demographic variables contribute minimally. The study provides a replicable and robust methodology aligned with risk analysis practices in student credit contexts.