Artificial intelligence in financial security markets: catalyzing sustainable development through innovation, risk mitigation, and adaptive governance

The rapid integration of artificial intelligence (AI) into financial security markets presents both significant opportunities and emerging governance challenges for sustainable development. This study employs a comparative mixed-methods approach to examine how AI-driven innovation in trading, risk management, regulatory compliance, and sustainability analytics interacts with institutional governance structures to shape sustainability outcomes and systemic risk exposure in financial markets. Through a comparative institutional analysis of leading financial systems in China, the United States, and the United Kingdom (2022–2025), we integrate secondary quantitative indicators with qualitative documentary evidence to explore how AI adoption is governed and operationalized across contrasting regulatory environments. The analysis indicates that AI-enabled financial innovation is associated with improvements in market efficiency, ESG integration, and risk assessment capabilities, while also introducing governance challenges related to model opacity, algorithmic bias, and the potential amplification of systemic vulnerabilities. The findings highlight the conditioning role of adaptive governance in shaping how AI-driven capabilities translate into sustainability and risk outcomes. Building on these insights, the study advances an integrative framework of sustainable AI governance that emphasizes regulatory adaptability, institutional coordination, and ethical oversight as critical mechanisms for aligning AI innovation with long-term financial stability and sustainability objectives. The framework offers policy-relevant guidance for regulators and financial institutions seeking to harness AI's transformative potential while managing its systemic implications.