Harnessing Fintech Innovations for Anti-Money Laundering: A Data-Driven Approach

Abstract

Blockchain technology offers significant advantages for trade finance by enhancing both efficiency and security through decentralized, automated, and robust systems. Research consistently highlights blockchain’s potential to drastically cut trade finance processing times— from the traditional span of 7–10 days down to as brief as 4 hours (Li, 2023)—and significantly reduce operational costs, exemplified by a striking 98% reduction in pilot initiatives like the World Food Programme case (Kellaf, 2024). Furthermore, recent technical demonstrations indicate that blockchain platforms can efficiently support high-volume operations, reaching throughput rates as high as 149.8 transactions per second (Zhang et al., 2024). Empirical simulation studies also reinforce these observations, estimating efficiency improvements of up to 94% compared to conventional systems (Asad et al., n.d.).

In addition to these efficiency enhancements, blockchain's features such as smart contracts, secure and immutable document storage, and encrypted transaction records significantly mitigate the risks of fraud. These mechanisms notably deter issues like double financing, forgery of trade documentation, and trade-based money laundering (Xu et al., 2022; Guerar et al., 2020; Rijanto, 2021). Specifically, rule-based detection frameworks and decentralized, transparent audit trails offer enhanced security and visibility throughout the trade lifecycle, effectively addressing historical vulnerabilities in traditional finance methods (Rantung et al., 2024).

Drawing from a comprehensive analysis of over 25 peer-reviewed case studies and technological demonstrations, this study synthesizes insights from diverse geographical and implementation contexts. The findings underscore blockchain technology as not merely a theoretical solution, but as a practical, scalable, and credible innovation capable of significantly modernizing the U.S. trade finance sector and bolstering defenses against financial crime. Distinctively, this research advances prior literature by empirically validating its claims through an original, controlled simulation. This simulation further substantiates blockchain's effectiveness in both efficiency optimization and fraud prevention, thus providing quantitative empirical backing to blockchain's promise in trade finance.

Keywords: Fintech, Anti-Money Laundering, Machine Learning, Federated Learning, Graph Neural Networks, Financial Crime, Compliance Technology, Explainable AI, RegTech