Artificial Intelligence in Predicting Bankruptcy and Financial Distress

Abstract

This study examines the application of Artificial Intelligence (AI) in predicting bankruptcy and financial distress using a mixed-method approach that integrates quantitative modeling with qualitative interpretability. Drawing on firm-level financial data, macroeconomic indicators, and textual disclosures, the research employs logistic regression as a baseline, alongside advanced AI techniques including Random Forest, Gradient Boosting Machines (GBM), Support Vector Machines (SVM), and Long Short-Term Memory (LSTM) networks. Results demonstrate that ensemble learning and deep learning models significantly outperform traditional approaches in predictive accuracy, F1-score, and area under the ROC curve, while also exhibiting greater robustness against class imbalance through techniques such as SMOTE.Key findings reveal that profitability, leverage, and liquidity remain the most influential predictors of bankruptcy, though AI methods capture nonlinear interactions and temporal dependencies overlooked by conventional models. Sectoral analyses indicate heightened risk in retail and energy industries, aligning with their cyclical sensitivities. Incorporation of Explainable AI (XAI) tools, including SHAP values, enhances interpretability by identifying the marginal contribution of features to model outputs, thereby addressing concerns over regulatory transparency and managerial trust. Complementary qualitative analysis of governance reports and market sentiment further contextualizes predictive outcomes, ensuring a more holistic framework for financial distress forecasting.The study contributes to the literature by validating the superiority of AI-driven hybrid approaches and emphasizing the necessity of interpretability in financial prediction. Practically, the findings highlight the potential of AI to support investors, regulators, and managers in early identification of distress signals, enabling proactive intervention and reducing systemic risks. Overall, the research confirms that AI, when rigorously applied and coupled with explainability, represents a transformative tool for advancing the accuracy, transparency, and utility of bankruptcy prediction in contemporary financial markets.