  Money laundering is the process where criminals use financial services to
move massive amounts of illegal money to untraceable destinations and integrate
them into legitimate financial systems. It is very crucial to identify such
activities accurately and reliably in order to enforce an anti-money laundering
(AML). Despite tremendous efforts to AML only a tiny fraction of illicit
activities are prevented. From a given graph of money transfers between
accounts of a bank, existing approaches attempted to detect money laundering.
In particular, some approaches employ structural and behavioural dynamics of
dense subgraph detection thereby not taking into consideration that money
laundering involves high-volume flows of funds through chains of bank accounts.
Some approaches model the transactions in the form of multipartite graphs to
detect the complete flow of money from source to destination. However, existing
approaches yield lower detection accuracy, making them less reliable. In this
paper, we employ semi-supervised graph learning techniques on graphs of
financial transactions in order to identify nodes involved in potential money
laundering. Experimental results suggest that our approach can sport money
laundering from real and synthetic transaction graphs.
