COMPARING ARTIFICIAL NEURAL NETWORKS & LOGISTIC REGRESSION FOR BANKRUPTCY PREDICTION: A RESEARCH SYNTHESIS

Abstract

Predicting corporate bankruptcy accurately is crucial for reducing financial risks and protecting stakeholders. Logistic Regression (LR) has long been valued for its interpretability, while Artificial Neural Networks (ANNs) have gained prominence for capturing complex, nonlinear financial relationships. This study synthesizes comparative evidence to assess their relative strengths and limitations. A systematic review of peer-reviewed studies published between 2018 and 2024 was conducted using Scopus, Web of Science, and IEEE Xplore. Studies were included if they directly compared ANNs and LR in bankruptcy prediction, reporting performance metrics such as accuracy, recall, and handling of class imbalance. Attention was also given to the application of visualization and interpretability tools. The findings indicate that ANNs generally outperform LR in predictive performance, with accuracy reaching up to 96% and recall above 95%, especially when advanced preprocessing techniques such as the Synthetic Minority Oversampling Technique (SMOTE) and feature selection are applied. ANNs are particularly effective in modelling nonlinear and high-dimensional data but face challenges related to overfitting, computational demand, and lack of transparency. LR, though often less accurate, remains robust for its statistical rigor, ease of implementation, and interpretability. In certain contexts, LR has also demonstrated higher precision, showing that performance may vary depending on the characteristics and quality of the underlying data. Overall, ANNs are preferable in complex, data-rich environments, while LR is more suitable where transparency and regulatory compliance are critical. Future research should explore hybrid models that integrate ANN’s predictive power with LR’s explainability, supported by explainable AI and real-time decision-support tools.