AI-Based Bankruptcy Prediction Using Financial Statements
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Corporate bankruptcy prediction is a critical component of financial risk management; however, traditional statistical models often struggle to process the volume and non-linear complexity of modern financial data. This study investigates the efficacy of Artificial Intelligence (AI) and machine learning techniques in predicting corporate bankruptcy using data extracted directly from financial statements. By evaluating key profitability, liquidity, leverage, and efficiency ratios, the research compares the predictive performance of AI-based models—such as decision trees and advanced classification algorithms—against traditional econometric approaches. The methodology involves training, testing, and cross-validating these models on historical financial data to identify hidden relational structures associated with financial distress. The empirical findings demonstrate that AI-based models significantly outperform traditional methods in predictive accuracy, adaptability, and the dynamic weighting of critical variables, with profitability and liquidity ratios emerging as the strongest indicators of financial stability. While the study highlights the transformative potential of AI as a proactive early warning system, it also addresses inherent challenges, including the "black box" nature of machine learning interpretability and a heavy reliance on pristine data quality. Ultimately, this research concludes that integrating AI into financial analysis fundamentally enhances corporate risk assessment, providing investors, auditors, and policymakers with a highly effective tool for safeguarding financial stability.
Keywords
Artificial Intelligence (AI), Bankruptcy Prediction, Corporate Distress, Financial Ratios, Financial Risk Management, Machine Learning, Predictive Analytics