Abstract
Online fraud has become an endemic threat in the modern digitally networked world, driven by the phenomenal expansion of e-commerce, digital banking and virtual identities. Traditional detection tools are no longer adequate to cope with the speed and complexity of contemporary fraud methods. This current research presents a critical analysis of emerging technologies and legal developments for combating online fraud in industries. Ensemble models like XGBoost and AdaBoost attain over 98% detection rates, while AI-powered behavioral analysis and deep learning architectures significantly enhance real-time anomaly detection in financial systems. The research also discusses fraud typologies like phishing, identity theft, payment scams, investment fraud and AI-based deception in the metaverse. Legal mechanisms, however, lag behind and are often in arrears relative to technology, resulting in enforcement issues due to uncertainty in jurisdictions, archaic laws and definitional difference. The study urges international cooperation, standardized digital forensics and cross-border legal unification to bridge the gaps. In addition, the role of user-centric security controls, cyber hygiene awareness and explainable AI (XAI) in enabling ethical and transparent fraud prevention is highlighted. A multidisciplinary framework integrating strong technology, revised legal policy and public participation emerges as a necessary model to contain and manage the dynamic threat landscape of online fraud.
Keywords: Artificial Intelligence, Blockchain Security, Cybercrime Detection, Legal Frameworks, Online Fraud Prevention