AI-Driven Risk Management: Strengthening Cybersecurity and Market Stability in the US Financial Sector

The rapid digital transformation of the United States' financial sector has intensified both opportunities and vulnerabilities, necessitating a paradigm shift in risk management strategies. This paper examines how Artificial Intelligence (AI) is reshaping risk management frameworks to enhance cybersecurity resilience and market stability across financial institutions. It explores the integration of machine learning algorithms, predictive analytics, and natural language processing tools to detect anomalies, forecast threats, and optimize decision-making in real time. Through an analytical review of recent implementations by major U.S. banks and regulatory agencies, the study highlights how AI-driven systems are mitigating cyber threats, reducing operational risks, and reinforcing compliance mechanisms under dynamic market conditions. Furthermore, the paper discusses ethical, regulatory, and governance challenges associated with AI adoption, emphasizing the need for transparent algorithms and human oversight. Findings suggest that AI not only strengthens the sector’s defensive capabilities but also contributes to systemic stability by enabling proactive identification of market disruptions. The study concludes that an integrated AI–risk management model, supported by adaptive regulation and cross-sector collaboration, is vital for sustaining trust and resilience in the evolving U.S. financial ecosystem.