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Abstract. This study examines the impact of AI-based risk management on financial market stability, utilizing both econometric analysis and real-world case studies. The research focuses on financial institutions such as JPMorgan Chase, Goldman Sachs, BlackRock, and others that have successfully implemented Artificial intelligence (AI) algorithms to analyze market trends and trading patterns, leading to more informed investment decisions and better overall portfolio performance. The econometric analysis reveals a positive and statistically significant relationship between AI-based risk management and financial market stability. Institutions leveraging AI technologies for risk management experience lower levels of volatility, better risk assessment, and improved decision-making, contributing to greater overall stability in financial markets. The study also identifies challenges faced by institutions implementing AI-based risk management systems, including the need for high-quality data, algorithm complexity, and regulatory compliance. To address these challenges and maximize the benefits of AI in risk management, several recommendations are proposed. These include investing in data quality and governance, enhancing regulatory frameworks, fostering collaboration and knowledge sharing, investing in employee training and development, monitoring and evaluating AI systems regularly, and considering the ethical and social implications of AI adoption. The findings suggest that AI-based risk management has the potential to significantly enhance financial market stability.
Aleksandra Kuzior (Mon,) studied this question.