AI in Anti-Money Laundering: A New Era of Financial Security in Commerce

Abstract

The increasing sophistication of international financial transactions has increased the possibility of money laundering and financial crime, calling into question the stability and trustworthiness of the commercial and banking sectors. Conventional AML systems, which rely on manual reviews and static rule-based procedures, have been shown to be ineffective at detecting complex fraudulent activity. In this setting, artificial intelligence emerges as a transformative solution to improving financial security. This study critically examines the importance of AI-powered systems in detecting suspicious transactions, improving regulatory compliance, and increasing fraud detection efficiency. This study used a descriptive and conceptual research design based on secondary data sources such as scholarly publications, financial records, and institutional case studies. The findings revealed that AI technologies have enhanced the accuracy and speed of detecting illicit financial behavior through predictive analytics, pattern recognition, and real-time monitoring. Second, it ensures better compliance with AML global standards and enhances transparency in financial operations. It is concluded from the study that integration of Artificial Intelligence into AML frameworks initiates a new generation of financial security in commerce whereby institutions can build resilience, trust, and accountability in the digital financial ecosystem.