Leading the development of AI-Driven AML and Compliance Infrastructure to Modernize U.S Financial Crime Prevention System Across Digital and Traditional Platforms

The persistent evolution of financial crime has outpaced the capabilities of conventional anti-money laundering (AML) frameworks, necessitating a technological shift toward intelligent systems. This review investigates the strategic development of artificial intelligence (AI)-driven AML and compliance infrastructure aimed at modernizing the United States financial crime prevention landscape across digital and traditional platforms. It outlines the technical foundations of machine learning, natural language processing, and anomaly detection algorithms that underpin next-generation surveillance tools. Emphasis is placed on the integration of AI into fintech ecosystems, cryptocurrency platforms, and legacy banking operations to ensure cross-platform compliance efficacy. The paper further examines the transformation of core compliance functions such as Know-Your-Customer (KYC), transaction monitoring, and regulatory reporting through automation and explainable AI models. Critical challenges including algorithmic bias, data privacy, and legal frameworks are evaluated, alongside a roadmap for national adoption involving public-private partnerships and regulatory harmonization. The study highlights the potential of AI to redefine financial oversight by enabling scalable, real-time, and adaptive systems aligned with evolving financial behaviors and regulatory expectations.