Artificial Intelligence in Securities Risk Management: Applications, Vulnerabilities, and Governance Frameworks

Abstract

The rapid development of artificial intelligence (AI) technologies has brought substantial convenience to the conduct of various business activities in securities firms, particularly in the field of risk management, where multiple large-scale models can be employed to address complex real-world problems. The application of AI techniques such as ensemble learning, deep learning and unsupervised learning in the risk management practices of securities firms makes it possible to process and analyze data rapidly, improve the efficiency and quality of decision making, and reduce labor costs, thereby enhancing the overall level of financial risk management. However, the adoption of these new technologies entails not only advantages but also notable drawbacks. The “black box” nature of many AI models weakens their interpretability, and the associated ethical issues, including data leakage and privacy protection, remain to be effectively resolved. These problems can in turn increase the difficulty and cost of regulation and may even trigger systemic risk. Consequently, AI technologies need to be properly aligned with and adapted to the financial system. In addition, all participants in financial markets should develop an appropriate understanding of AI and apply it in a prudent manner, so as to enable financial technology to better serve the functioning and development of financial markets.