Anti-Money Laundering with Unreliable Labels

Abstract

This report examines the effectiveness of Graph Neural Networks (GNNs) in detecting money laundering activities using transaction data with unreliable labels. It explores how weakly supervised learning, specifically with GNNs, manages the challenges posed by missing and inaccurate labels in anti-money laundering (AML) systems. The study utilizes simulated transaction datasets to compare the performance of GNNs against traditional statistical models. Findings indicate that GNNs, due to their ability to process relational data structures, demonstrate superior adaptability and accuracy in scenarios with label deficiencies. This research provides effective strategies for enhancing anti-money laundering systems by employing GNNs to more effectively manage data challenges.