Money laundering presents a pervasive challenge, burdening society by financing illegal activities. The use of network information is increasingly being explored to effectively combat money laundering given that it involves connected parties. This led to a surge in research on network analytics for anti-money laundering (AML). The literature is, however, fragmented, and a comprehensive overview of existing work is missing. This results in limited understanding of the methods to apply and their comparative detection power. This paper presents an extensive and unique literature review based on 97 papers from Web of Science and Scopus, resulting in a taxonomy following a recently proposed fraud analytics framework. We conclude that most research relies on expert-based rules and manual features, whereas deep learning methods have been gaining traction. This paper also presents a comprehensive framework to evaluate and compare the performance of prominent methods in a standardized setup. We compare manual feature engineering, random walk-based, and deep learning methods on two publicly available data sets. We conclude (1) that network analytics increases the predictive power but caution is needed when applying graph neural networks in the face of class imbalance and network topology and (2) that care should be taken with synthetic data as they can give overly optimistic results. The open-source implementation facilitates researchers and practitioners to extend this work on proprietary data, promoting a standardized approach for the analysis and evaluation of network analytics for AML. History: Galit Shmueli served as the senior editor for this article. Funding: This work was supported by Fonds Wetenschappelijk Onderzoek Grants 1SHEN24N and G015020N and the BNP Paribas Fortis Grant Research Chair on Fraud Analytics. The resources and services used in this work were provided by the Flemish Supercomputer Center funded by the Fonds Wetenschappelijk Onderzoek and the Flemish Government. Supplemental Material: The online appendix is available at https://doi.org/10.1287/ijds.2024.0042 .
Deprez et al. (2025) studied this question.