  Given a stream of money transactions between accounts in a bank, how can we
accurately detect money laundering agent accounts and suspected behaviors in
real-time? Money laundering agents try to hide the origin of illegally obtained
money by dispersive multiple small transactions and evade detection by smart
strategies. Therefore, it is challenging to accurately catch such fraudsters in
an unsupervised manner. Existing approaches do not consider the characteristics
of those agent accounts and are not suitable to the streaming settings.
Therefore, we propose MonLAD and MonLAD-W to detect money laundering agent
accounts in a transaction stream by keeping track of their residuals and other
features; we devise AnoScore algorithm to find anomalies based on the robust
measure of statistical deviation. Experimental results show that MonLAD
outperforms the state-of-the-art baselines on real-world data and finds various
suspicious behavior patterns of money laundering. Additionally, several
detected suspected accounts have been manually-verified as agents in real money
laundering scenario.
