AI IN FRAUD DETECTION AND REGULATORY COMPLIANCE

The increasing sophistication of financial fraud and the complexity of regulatory requirements have driven organizations to integrate artificial intelligence (AI) into fraud detection and compliance systems. This research explores the application of AI, particularly machine learning and natural language processing, in identifying fraudulent activities across domains such as finance, healthcare, and cybersecurity. It examines rule-based and deep learning models, real-time detection capabilities, and human-in-the-loop systems, supported by case studies from leading institutions. The study also addresses key ethical challenges including data bias, lack of explainability, and privacy risks. It concludes with an overview of emerging trends such as Explainable AI (XAI) and federated learning, emphasizing the importance of transparent, scalable, and adaptive AI solutions in ensuring both fraud prevention and regulatory adherence.