Learning from Financial Transactions with Graph Neural Networks for Anti-Money Laundering
PI: Dr. Qi Liu, HKU
Co-I: Prof. S.M. Yiu, HKU
Money laundering is the process of concealing the origin of money, often obtained from crimes such as drug dealing and human trafficking, by converting it into a legitimate source. Money laundering results in around 2-5% of global GDP (1.7-4 trillion euros) being laundered annually (Lannoo and Parlour 2021). Anti-money laundering (AML) refers to the legal controls that require financial institutions and other regulated entities to prevent, detect, and report money laundering activities. The AML systems deployed by financial institutions typically comprise rules aligned with regulatory frameworks. Human investigators review the alerts and report suspicious cases. Such systems suffer from high false-positive rates, undermining their effectiveness and resulting in high operational costs. In this project, we investigate graph neural networks (GNNs) for AML. GNNs have shown great promise in learning from relational data for a wide range of predictive tasks. GNN as a universal approximator can reduce the burden of manually designing rules. The goal of this project is to design new GNNs models that are not only accurate in predicting money laundering, but also self-explainable via graph intervention and counterfactual inference.