  Money laundering presents a pervasive challenge, burdening society by
financing illegal activities. To more effectively combat and detect money
laundering, the use of network information is increasingly being explored,
exploiting that money laundering necessarily involves interconnected parties.
This has lead to a surge in literature on network analytics (NA) for anti-money
laundering (AML). The literature, however, is fragmented and a comprehensive
overview of existing work is missing. This results in limited understanding of
the methods that may be applied and their comparative detection power.
Therefore, this paper presents an extensive and systematic review of the
literature. We identify and analyse 97 papers in the Web of Science and Scopus
databases, resulting in a taxonomy of approaches following the fraud analytics
framework of Bockel-Rickermann et al.. Moreover, this paper presents a
comprehensive experimental framework to evaluate and compare the performance of
prominent NA methods in a uniform setup. The framework is applied on the
publicly available Elliptic data set and implements manual feature engineering,
random walk-based methods, and deep learning GNNs. We conclude from the results
that network analytics increases the predictive power of the AML model with
graph neural networks giving the best results. An open source implementation of
the experimental framework is provided to facilitate researchers and
practitioners to extend upon these results and experiment on proprietary data.
As such, we aim to promote a standardised approach towards the analysis and
evaluation of network analytics for AML.
