Transcending Rule-Based Compliance: The Impact of Artificial Intelligence on Anti-Money Laundering and Know Your Customer Frameworks within Digital Payment Ecosystems

Abstract

The global financial system is losing a lot of funds to money laundering. This loss is estimated at about $800 billion to $2 trillion yearly, with digital payment channels increasingly adopted to illicit these transfers. Traditional Anti-Money Laundering (AML) and Know Your Customer (KYC) compliance architectures, based on fixed transaction thresholds, periodic batch customer screening, and manual Suspicious Activity Report (SAR) generation, are inadequate to address the speed, cross-border complexity, and adaptive sophistication of financial crime in real-time. This paper offers a theoretically grounded analysis of the transformation of AML and KYC compliance through artificial intelligence (AI) and machine learning (ML) in digital payment ecosystems. It draws on regulatory compliance theory, network theory and dynamic capabilities theory to contend that AI-enabled compliance is a paradigmatic institutional transformation, rather than an incremental operational improvement. Comparatively, AI systems reduced false positive alert volumes by 40 to 55%, average SAR investigation time by 60% and improved suspicious activity detection rates by 35 to 47% above rule-based predecessors. Three case studies; examining Danske Bank's anti-money laundering failure and its subsequent AI remediation efforts, HSBC's adoption of behavioral analytics for transaction monitoring, and Nigeria's Central Bank's AI-driven real-time settlement supervision, demonstrate the repercussions of inadequate compliance systems and the benefits of AI-driven transformation. The paper concludes with strategic roadmaps for institutions shifting from traditional rule-based compliance systems to adaptive, AI-driven AML and KYC frameworks, while also identifying key research directions for the empirical examination of AI compliance results across various regulatory jurisdictions