  Money launderers exploit the weaknesses in detection systems by purposefully
placing their ill-gotten money into multiple accounts, at different banks. That
money is then layered and moved around among mule accounts to obscure the
origin and the flow of transactions. Consequently, the money is integrated into
the financial system without raising suspicion. Path finding algorithms that
aim at tracking suspicious flows of money usually struggle with scale and
complexity. Existing community detection techniques also fail to properly
capture the time-dependent relationships. This is particularly evident when
performing analytics over massive transaction graphs. We propose a framework
(called FaSTMAN), adapted for domain-specific constraints, to efficiently
construct a temporal graph of sequential transactions. The framework includes a
weighting method, using 2nd order graph representation, to quantify the
significance of the edges. This method enables us to distribute complex queries
on smaller and densely connected networks of flows. Finally, based on those
queries, we can effectively identify networks of suspicious flows. We
extensively evaluate the scalability and the effectiveness of our framework
against two state-of-the-art solutions for detecting suspicious flows of
transactions. For a dataset of over 1 Billion transactions from multiple large
European banks, the results show a clear superiority of our framework both in
efficiency and usefulness.
