Research Review on the Application of Artificial Intelligence in Financial Fraud Detection and Risk Early Warning

Abstract

 As corporate financial environments grow increasingly complex, financial fraud has become a major threat to economic stability. Traditional detection methods are increasingly inadequate in addressing emerging fraud tactics. Leveraging its powerful data processing and pattern recognition capabilities, artificial intelligence technology offers new solutions for financial risk prevention. This paper systematically reviews research on AI applications in financial fraud identification and risk early warning, focusing on analyzing the characteristics and practical applications of key techniques such as supervised learning, unsupervised learning, and deep learning. Practical experience shows that AI-based early warning models can effectively detect abnormal financial indicators and hidden fraudulent activities. Current research still faces challenges including high dependence on data quality and low model interpretability. Future efforts should explore multimodal data integration and lightweight model deployment to promote deeper application and innovative development of AI technology in financial risk control.