Advanced Anti-Money Laundering (AML) frameworks for U.S. Fintech platforms

Abstract

The rapid expansion of U.S. financial technology platforms has created new vectors for money laundering, terrorist financing and financial crime that traditional anti-money laundering frameworks were not designed to address. This article presents a systematic literature review of 78 peer-reviewed studies published between 2015 and 2025 to examine the design, performance and policy implications of advanced anti-money laundering frameworks for U.S. fintech platforms. This study draws on evidence from financial criminology, regulatory law, computer science and organizational studies; the review finds that machine learning-based transaction monitoring systems reduce false positive alert rates by 40 to 70 percent compared to rule-based systems, as well as improving detection of sophisticated layering schemes. Blockchain analytics tools partially de-anonymize cryptocurrency transaction flows and have been used to identify illicit financial activity on major blockchain networks. Regulatory technology platforms automate suspicious activity reporting, beneficial ownership identification and customer due diligence workflows in ways that reduce compliance costs as well as improve regulatory data quality. However, the reviewed literature also documents persistent challenges, including algorithmic disparate impact in AML monitoring systems, beneficial ownership opacity through shell company structures, regulatory arbitrage between licensed exchanges and decentralized finance protocols and the systemic underutilization of suspicious activity report intelligence by law enforcement agencies. The article concludes with six evidence-based policy recommendations and a research agenda for advancing AML framework effectiveness in the rapidly evolving U.S. fintech sector. Keywords: Anti-Money Laundering, Fintech, AML Compliance, Machine Learning, Transaction Monitoring, Know Your Customer, Cryptocurrency Regulation, Regulatory Technology, Suspicious Activity Reporting, Financial Crime.