Theoretical Framework and Risk Analysis Research on the Application of Artificial Intelligence in Financial Risk Management

Abstract

The theoretical framework for the application of artificial intelligence in financial risk management and related risk analysis are what this paper aims to explore. Due to the increasing complexity of financial markets and the explosive growth of data volume, traditional risk management methods have become difficult to meet the demands of the modern financial system, while artificial intelligence technology has strong data processing, pattern recognition and prediction capabilities. It is gradually taking an important position in financial risk management. This study first constructs a theoretical framework for the application of artificial intelligence in financial risk management, covering four major areas: market risk, credit risk, operational risk, and liquidity risk. At the same time, it analyzes the specific application mechanisms of AI technologies such as machine learning, deep learning, and natural language processing in these four risk areas. Secondly, the article delves deeply into potential risks such as algorithmic black boxes, data privacy protection, poor model interpretability, and systemic risk amplification when AI technology is applied to financial risk management. After constructing a three-dimensional assessment model of "technology - application - risk", a complete set of risk prevention and control measures and regulatory recommendations are proposed. The research shows that AI technology has significant advantages in improving the accuracy of financial risk identification, the timeliness of early warning, and the efficiency of management. However, its application is based on a sound risk assessment and regulatory framework. This paper can provide theoretical references and practical guidance for financial institutions to effectively integrate AI technology for risk management and for regulatory authorities to formulate adaptive policies.