Graph-Based Data Architecture for Multi-Institutional AML Pattern Discovery Using Distributed Ledger Analytics

Abstract

In the interconnected world of finance today, fighting money laundering is a paramount issue for institutions globally. This paper presents a new graph-based data architecture to enable multi-institutional pattern identification in anti-money laundering (AML) activities using the capabilities of distributed ledger analytics. With a real-world dataset of transaction records and suspicious activity reports, our solution builds complex networks that uncover hidden connections among distant financial institutions. The architecture facilitates dynamic visualization and anomaly detection on intricate transaction graphs but also leverages the intrinsic security and transparency of distributed ledger technology to provide data integrity and collaborative insights. Through intensive testing, the framework demonstrates improved detection accuracy and reduced false positives, offering an efficient and scalable way for regulators and financial institutions striving to detect and prevent financial crime threats in real time.