AI for Financial Fraud Detection: Leveraging Machine Learning for Risk Mitigation

Abstract

Financial institutions are using AI to detect fraudulent transactions and prevent financial crimes. This paper explores AI models such as deep learning, graph neural networks, and anomaly detection for fraud prevention. It discusses challenges related to data security, model explainability, and adversarial attacks. Case studies highlight AI-powered fraud detection systems, demonstrating their effectiveness in identifying suspicious activities, reducing financial losses, and ensuring regulatory compliance.