Network Analytics for Anti-Money Laundering -- A Systematic Literature Review and Experimental Evaluation
<|reference_start|>Network Analytics for Anti-Money Laundering -- A Systematic Literature Review and Experimental Evaluation: 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.<|reference_end|>
@article{deprez2024network,
    title={Network Analytics for Anti-Money Laundering -- A Systematic Literature
  Review and Experimental Evaluation},
    author={Bruno Deprez, Toon Vanderschueren, Bart Baesens, Tim Verdonck, Wouter
  Verbeke},
    journal={arXiv preprint arXiv:2405.19383},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.19383},
    primaryClass={cs.SI cs.LG}
}