The Role of AI in Financial Fraud Detection: A Review of Machine Learning Approaches

Abstract

Financial fraud detection has significantly improved with AI-powered anomaly detection and predictive analytics. This paper reviews various machine learning techniques used in detecting credit card fraud, money laundering, and insider trading. We analyze the effectiveness of supervised and unsupervised learning approaches, including neural networks, decision trees, and clustering algorithms. Challenges such as false positives, adversarial fraud strategies, and regulatory compliance are also discussed. Future research directions focus on real-time detection and blockchain integration.