Predictive Analytics for Corporate Financial Distress: A Machine Learning Framework for Early Warning Systems

Posted: Oct 28, 2025

• Wei Zhang
• Maria Rodriguez
• Kenji Tanaka
• Fatima Al-Mansoori

Abstract

This research develops a comprehensive machine learning framework for predicting corporate financial distress using accounting and market data. We employ multiple classification algorithms including logistic regression, random forests, and support vector machines to identify early warning signals of financial distress. Our dataset comprises 2,500 publicly traded companies across various sectors from 1995 to 2003. The proposed framework achieves 94.2% accuracy in predicting financial distress 12 months prior to occurrence, significantly outperforming traditional statistical methods. Feature importance analysis reveals that cash flow ratios, debt coverage metrics, and market-based indicators are the most significant predictors. The study contributes to both accounting literature and financial risk management practice by providing a robust, data-driven approach to corporate financial health assessment.