  Money laundering is a major global problem, enabling criminal organisations
to hide their ill-gotten gains and to finance further operations. Prevention of
money laundering is seen as a high priority by many governments, however
detection of money laundering without prior knowledge of predicate crimes
remains a significant challenge. Previous detection systems have tended to
focus on individuals, considering transaction histories and applying anomaly
detection to identify suspicious behaviour. However, money laundering involves
groups of collaborating individuals, and evidence of money laundering may only
be apparent when the collective behaviour of these groups is considered. In
this paper we describe a detection system that is capable of analysing group
behaviour, using a combination of network analysis and supervised learning.
This system is designed for real-world application and operates on networks
consisting of millions of interacting parties. Evaluation of the system using
real-world data indicates that suspicious activity is successfully detected.
Importantly, the system exhibits a low rate of false positives, and is
therefore suitable for use in a live intelligence environment.
