Utilizing Artificial Intelligence for Financial Risk Monitoring in Asset Management

This paper explores the application and advantages of artificial intelligence (AI) in financial risk monitoring in asset management. With the increasing dynamics and complexity of the financial market, traditional risk management methods are insufficient to cope with the changing market environment. To this end, this paper assesses how AI technologies, specifically machine learning and deep learning models, can provide new solutions for risk prediction, identification, and management. The paper first reviews the traditional methods of financial risk monitoring, and their limitations then introduces the core concepts and techniques of artificial intelligence technology, including supervised, unsupervised, deep, and others. Through a systematic analysis of existing AI application cases, this paper explores how these technologies can improve the accuracy of risk identification, optimize asset allocation, and enhance the effectiveness of investment decisions. In particular, deep learning models have demonstrated a strong ability to process complex data patterns and predict market trends, enabling asset management to respond more quickly and accurately to unexpected events. The paper also discusses the practical application challenges of AI in financial risk monitoring, including data quality issues, interpretability of models, and implementation costs of the technology. This paper summarizes the best practices of AI technology in asset management through a comprehensive case study and data analysis. It proposes future research directions and policy recommendations to promote further the technology development and application promotion in this field.