Abstract:

With the rapid growth of online transactions and interactions, the threat landscape of scams and fraud has evolved, necessitating sophisticated detection mechanisms. This paper provides an extensive review of the latest advances in detecting online scams and fraud, covering technological solutions, machine learning techniques, and emerging trends in the field. Key methods discussed include advanced machine learning algorithms for anomaly detection, user behavior analytics, and the integration of threat intelligence. Additionally, the study highlights the role of public awareness and education in preventing scams, as well as the importance of international collaboration in law enforcement. By examining current trends and emerging technologies, this study provides strategies for organizations and individuals to enhance their digital security posture, effectively mitigating the risks associated with online scams and frauds.