LineMVGNN: Anti-Money Laundering with Line-Graph-Assisted Multi-View Graph Neural Networks
arXiv:2603.23584v1 Announce Type: new Abstract: Anti-money laundering (AML) systems are important for protecting the global economy. However, conventional rule-based methods rely on domain knowledge, leading to suboptimal accuracy and a lack of scalability. Graph neural networks (GNNs) for digraphs (directed graphs) can be applied to transaction graphs and capture suspicious transactions or accounts. However, most spectral GNNs do not naturally support multi-dimensional edge features, lack interpretability due to edge modifications, and have limited scalability owing to their spectral nature. Conversely, most spatial methods may not capture the money flow well. Therefore, in this work, we propose LineMVGNN (Line-Graph-Assisted Multi-View Graph Neural Network), a novel spatial method that considers payment and receipt transactions. Specifically, the LineMVGNN model extends a lightweight MVGNN module, which performs two-way message passing between nodes in a transaction graph. Additiona
arXiv:2603.23584v1 Announce Type: new Abstract: Anti-money laundering (AML) systems are important for protecting the global economy. However, conventional rule-based methods rely on domain knowledge, leading to suboptimal accuracy and a lack of scalability. Graph neural networks (GNNs) for digraphs (directed graphs) can be applied to transaction graphs and capture suspicious transactions or accounts. However, most spectral GNNs do not naturally support multi-dimensional edge features, lack interpretability due to edge modifications, and have limited scalability owing to their spectral nature. Conversely, most spatial methods may not capture the money flow well. Therefore, in this work, we propose LineMVGNN (Line-Graph-Assisted Multi-View Graph Neural Network), a novel spatial method that considers payment and receipt transactions. Specifically, the LineMVGNN model extends a lightweight MVGNN module, which performs two-way message passing between nodes in a transaction graph. Additionally, LineMVGNN incorporates a line graph view of the original transaction graph to enhance the propagation of transaction information. We conduct experiments on two real-world account-based transaction datasets: the Ethereum phishing transaction network dataset and a financial payment transaction dataset from one of our industry partners. The results show that our proposed method outperforms state-of-the-art methods, reflecting the effectiveness of money laundering detection with line-graph-assisted multi-view graph learning. We also discuss scalability, adversarial robustness, and regulatory considerations of our proposed method.
Executive Summary
This article proposes LineMVGNN, a novel spatial method for anti-money laundering (AML) detection using line-graph-assisted multi-view graph neural networks. The model combines a lightweight MVGNN module with a line graph view of the transaction graph to enhance information propagation. Experimental results on two real-world datasets demonstrate the effectiveness of LineMVGNN in detecting money laundering, outperforming state-of-the-art methods. The proposed method also exhibits scalability, adversarial robustness, and regulatory considerations. This research contributes to the development of more accurate and efficient AML systems, which are crucial for protecting the global economy from financial crimes. The findings have significant implications for both practical applications and policy-making in the field of AML.
Key Points
- ▸ LineMVGNN is a novel spatial method for AML detection using line-graph-assisted multi-view graph neural networks.
- ▸ The model combines a lightweight MVGNN module with a line graph view of the transaction graph to enhance information propagation.
- ▸ Experimental results demonstrate the effectiveness of LineMVGNN in detecting money laundering, outperforming state-of-the-art methods.
Merits
Strength
LineMVGNN addresses the limitations of conventional rule-based methods and spectral GNNs in AML detection, offering improved accuracy and scalability.
Interpretability
The proposed method incorporates a line graph view of the transaction graph, allowing for better understanding and visualization of transaction information.
Scalability
LineMVGNN demonstrates scalability in handling large transaction graphs and datasets, making it suitable for practical applications.
Demerits
Limitation
The proposed method may not capture the nuances of specific financial crimes or regulatory requirements, requiring further customization or adaptation.
Data Requirements
LineMVGNN relies on high-quality and comprehensive transaction data, which may not be readily available or easily accessible in all scenarios.
Expert Commentary
The proposed LineMVGNN method offers a promising solution for AML detection, leveraging the strengths of graph neural networks and line graph views to enhance information propagation. The experimental results demonstrate its effectiveness in detecting money laundering, outperforming state-of-the-art methods. However, further research is needed to address the limitations and potential challenges associated with the proposed method, such as data requirements and regulatory considerations. Additionally, the scalability and interpretability of LineMVGNN should be further explored to ensure its practical applicability and regulatory compliance.
Recommendations
- ✓ Future research should focus on adapting LineMVGNN to specific financial crimes and regulatory requirements, ensuring its practical applicability and regulatory compliance.
- ✓ Further investigation is needed to explore the scalability and interpretability of LineMVGNN, addressing potential challenges and limitations associated with its implementation.
Sources
Original: arXiv - cs.LG
