Network Analytics for Anti-Money Laundering -- A Systematic Literature Review and Experimental Evaluation

TL;DR

This paper provides a comprehensive review and experimental comparison of network analytics methods for anti-money laundering, highlighting the strengths and limitations of various approaches including deep learning, and offering a standardized evaluation framework.

Contribution

It offers the first extensive literature review with a taxonomy, and introduces a standardized framework for evaluating network analytics methods in AML.

Findings

Network analytics improve detection accuracy.

Deep learning methods are increasingly popular.

Caution needed with synthetic data and GNNs in imbalanced scenarios.

Abstract

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 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, while 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 that (1) network analytics increases the predictive power, but caution is needed when applying GNNs in the face of class imbalance and network topology, and that (2) care should be taken with synthetic data as this can give overly optimistic results. The open-source implementation facilitates researchers and practitioners to extend this work on proprietary data, promoting a standardised approach for the analysis and evaluation of network analytics for AML.