Key points are not available for this paper at this time.
Financial stability is a critical pillar of economic resilience, particularly in the face of market uncertainty driven by global disruptions, technological shifts, and evolving regulatory landscapes. Traditional risk assessment models often struggle to adapt to the increasing complexity and speed of financial markets. Artificial Intelligence (AI)-driven risk assessment frameworks offer a transformative solution by leveraging machine learning, predictive analytics, and real-time data processing to enhance financial decision-making. These AI-powered models can detect emerging risks, identify market anomalies, and optimize portfolio strategies, providing financial institutions with a proactive approach to mitigating volatility. The integration of AI in financial risk management enhances the accuracy of credit scoring, fraud detection, and liquidity analysis, reducing systemic vulnerabilities and improving investor confidence. Additionally, AI-driven sentiment analysis and natural language processing (NLP) enable financial analysts to interpret market signals more effectively, offering insights into economic trends and investment opportunities. Despite its advantages, AI adoption in financial stability assessments faces challenges such as algorithmic bias, regulatory compliance, and data privacy concerns. Addressing these limitations requires a balanced approach that incorporates ethical AI practices, transparent decision-making frameworks, and robust cybersecurity measures. This paper explores the role of AI in financial stability, focusing on its impact on market risk assessments, investment strategies, and regulatory compliance. By examining case studies of AI-driven financial decision-making, it highlights the potential of intelligent risk assessment models in mitigating market uncertainty. The findings emphasize the need for continued collaboration between policymakers, financial institutions, and AI researchers to harness technology for a more resilient and adaptive financial ecosystem.
Omopariola et al. (Sat,) studied this question.