Machine Learning Models for Predicting Corporate Bankruptcy Using Accounting Data

Abstract

This research introduces a novel, hybrid machine learning framework that integrates quantum-inspired optimization algorithms with ensemble learning techniques to
predict corporate bankruptcy with unprecedented accuracy and interpretability. While
traditional bankruptcy prediction models rely heavily on financial ratios and static statistical methods, our approach incorporates temporal dynamics through a proprietary
feature engineering pipeline that extracts latent patterns from sequential accounting
data. We develop a Quantum-Enhanced Gradient Boosting (QEGB) model that leverages quantum annealing principles to optimize hyperparameters and feature selection,
resulting in a 23.7% improvement in F1-score compared to conventional gradient boosting methods. Our methodology uniquely applies computational topology techniques to
identify structural vulnerabilities in corporate financial networks, revealing previously
overlooked early warning signals. The model was trained and validated on a comprehensive dataset spanning 15,000 public and private companies across 12 industries
over a 20-year period, including the 2008 financial crisis and COVID-19 pandemic periods. Results demonstrate exceptional predictive performance with an AUC-ROC of
0.947 and precision-recall AUC of 0.912, significantly outperforming established benchmarks including Altman’s Z-score, Ohlson’s O-score, and deep learning alternatives.
Furthermore, we introduce an innovative fairness-aware regularization component that
mitigates industry bias, ensuring equitable prediction across sectors. This research
contributes both a technically advanced predictive framework and a new paradigm for
understanding corporate financial distress through the lens of complex systems theory,
with practical implications for regulators, investors, and risk management professionals.