Investigating the Role of Artificial Intelligence in Predicting Financial Crises and Enhancing Risk Management in Global Markets

The increasing complexity, interconnectedness, and volatility of global financial markets have exposed the limitations of traditional risk management and crisis prediction approaches. This study investigates the effectiveness of artificial intelligence–driven models in enhancing financial risk forecasting and stability through an experimental mixed-method framework. Using large-scale financial, macroeconomic, and market-based datasets, multiple machine learning and deep learning models were developed, trained, and evaluated under identical experimental conditions. The empirical results demonstrate that AI-based models consistently outperform conventional statistical techniques in terms of predictive accuracy, robustness, and adaptability, particularly in capturing non-linear dependencies and emerging vulnerabilities within financial systems. The findings further reveal that AI-powered real-time risk monitoring significantly reduces anomaly detection time, thereby improving responsiveness during periods of market stress. Comparative analysis across nine performance tables and twelve complex visualizations confirms the reliability and generalizability of the proposed framework across diverse risk scenarios. Despite these performance gains, the study identifies key implementation challenges, including model interpretability, data governance, and integration with existing financial infrastructures. Overall, the results confirm that artificial intelligence represents a transformative tool for proactive financial risk management and crisis prevention, offering substantial improvements in decision accuracy, resilience, and regulatory effectiveness. The study provides valuable empirical insights for financial institutions, regulators, and policymakers seeking to harness AI for sustainable financial stability.