  Money laundering has become one of the most relevant criminal activities in
modern societies, as it causes massive financial losses for governments, banks
and other institutions. Detecting such activities is among the top priorities
when it comes to financial analysis, but current approaches are often costly
and labor intensive partly due to the sheer amount of data to be analyzed.
Hence, there is a growing need for automatic anti-money laundering systems to
assist experts. In this work, we propose DELATOR, a novel framework for
detecting money laundering activities based on graph neural networks that learn
from large-scale temporal graphs. DELATOR provides an effective and efficient
method for learning from heavily imbalanced graph data, by adapting concepts
from the GraphSMOTE framework and incorporating elements of multi-task learning
to obtain rich node embeddings for node classification. DELATOR outperforms all
considered baselines, including an off-the-shelf solution from Amazon AWS by
23% with respect to AUC-ROC. We also conducted real experiments that led to the
discovery of 7 new suspicious cases among the 50 analyzed ones, which have been
reported to the authorities.
