Finding Money Launderers Using Heterogeneous Graph Neural Networks
Current anti-money laundering (AML) systems, predominantly rule-based,
exhibit notable shortcomings in efficiently and precisely detecting instances
of money laundering. As a result, there has been a recent surge toward
exploring alternative approaches, particularly those utilizing machine
learning. Since criminals often collaborate in their money laundering
endeavors, accounting for diverse types of customer relations and links becomes
crucial. In line with this, the present paper introduces a graph neural network
(GNN) approach to identify money laundering activities within a large
heterogeneous network constructed from real-world bank transactions and
business role data belonging to DNB, Norway's largest bank. Specifically, we
extend the homogeneous GNN method known as the Message Passing Neural Network
(MPNN) to operate effectively on a heterogeneous graph. As part of this
procedure, we propose a novel method for aggregating messages across different
edges of the graph. Our findings highlight the importance of using an
appropriate GNN architecture when combining information in heterogeneous
graphs. The performance results of our model demonstrate great potential in
enhancing the quality of electronic surveillance systems employed by banks to
detect instances of money laundering. To the best of our knowledge, this is the
first published work applying GNN on a large real-world heterogeneous network
for anti-money laundering purposes.
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