Privacy-preserving graph-based machine learning with fully homomorphic encryption for collaborative anti-money laundering - Effendi, Fabrianne (Nanyang Technological University, 2024)
With the increasing digitalization of financial transactions and the rise of cybercrime, combating money laundering has become increasingly complex. Graph-based machine learning techniques have emerged as promising tools for Anti-Money Laundering (AML) detection, capable of capturing intricate relationships within money laundering networks. However, the effectiveness of AML solutions is hindered by the challenge of data silos within financial institutions, limiting collaboration and reducing overall efficacy.