  Money laundering (ML) is the behavior to conceal the source of money achieved
by illegitimate activities, and always be a fast process involving frequent and
chained transactions. How can we detect ML and fraudulent activity in large
scale attributed transaction data (i.e.~tensors)? Most existing methods detect
dense blocks in a graph or a tensor, which do not consider the fact that money
are frequently transferred through middle accounts. CubeFlow proposed in this
paper is a scalable, flow-based approach to spot fraud from a mass of
transactions by modeling them as two coupled tensors and applying a novel
multi-attribute metric which can reveal the transfer chains accurately.
Extensive experiments show CubeFlow outperforms state-of-the-art baselines in
ML behavior detection in both synthetic and real data.
