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

2024-05-29

arXiv.org

AI Summary

Anti-money laundering (AML) research leveraging network analysis (NA) is fragmented, lacking systematic surveys and comparable empirical evaluations. Method: We conduct the first large-scale systematic literature review covering 97 papers and an accompanying empirical study. We propose the first structured taxonomy for NA-AML, develop a reproducible and extensible standardized benchmark, and uniformly evaluate three representative method classes—handcrafted features, random-walk-based embeddings (DeepWalk, Node2Vec), and graph neural networks (GNNs)—on public AML datasets. Contribution/Results: Our findings show that NA significantly improves money laundering detection performance; however, GNNs exhibit limited robustness under class imbalance and complex graph topologies. Moreover, existing open-source AML datasets suffer from representativeness bias. This work establishes a theoretical framework, an evaluation paradigm, and open-source tools for NA-AML, thereby advancing method standardization and reproducible research.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Money laundering presents a pervasive challenge, burdening society by financing illegal activities. The use of network information is increasingly being explored to more effectively combat money laundering, given it involves connected parties. This led to a surge in research on network analytics (NA) for anti-money laundering (AML). The literature on NA for AML 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. Therefore, 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 NA methods in a standardized setup. We apply it on two publicly available data sets, comparing manual feature engineering, random walk-based, and deep learning methods. We conclude that (1) network analytics increases the predictive power, but caution is needed when applying GNNs based on the class imbalance and network topology, and that (2) care should be taken with open-source data as this can give overly optimistic results. The open-source implementation facilitates researchers and practitioners to extend upon the results and experiment on proprietary data, promoting a standardized approach for the analysis and evaluation of network analytics for AML.

Problem

• Addresses fragmented research on network analytics for anti-money laundering.
• Evaluates and compares performance of network analytics methods.
• Highlights challenges in applying deep learning and open-source data.

Innovation

• Systematic literature review on network analytics
• Standardized framework for method evaluation
• Comparison of manual and deep learning methods