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Our aim in this paper is to detect bank clients involved in suspicious activities related to money laundering, using the graph of transactions of the bank. Although we have a labeled real dataset, our target is not only to obtain relevant results on it, but also on random graphs in which typical anomaly patterns have been injected. So, we want simultaneously adequacy to the real data and robustness. Our method is based on designing new features; the most important are those resulting from the reduced egonet, which is the subgraph that remains from an egonet after eliminating the nodes connected with a single edge to the center; another feature is built by appealing to random walks and serves as indicator of circular flows. Our features are added to usual egonet features and a general anomaly detection algorithm, in our case Isolation Forest, serves to detect the anomalies. Experiments on the real data and a comprehensive set of synthetic data show that our approach is adequate, robust and better than some previous methods.
Dumitrescu et al. (Sat,) studied this question.