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Bitcoin Money Laundering Detection via Subgraph Contrastive Learning.

Shiyu Ouyang¹, Qianlan Bai², Hui Feng¹

¹School of Information Science and Technology, Fudan University, Shanghai 200433, China.

Entropy (Basel, Switzerland)

|March 28, 2024

Summary

This summary is machine-generated.

This study introduces Bit-CHetG, a novel graph learning method to detect cryptocurrency money laundering groups by analyzing behavioral patterns. It significantly improves detection accuracy over existing methods.

Keywords:

Bitcoin anti-money laundering contrastive learning graph neural network heterogeneous graph

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Area of Science:

Computer Science
Cybersecurity
Data Science

Background:

Cryptocurrency proliferation has escalated money laundering activities.
Existing fraud detection often focuses on individual transactions, missing group behaviors.
Organized, heterogeneous, and noisy data in Bitcoin money laundering pose detection challenges.

Purpose of the Study:

To develop a novel algorithm for detecting money laundering groups in Bitcoin.
To address the limitations of node classification in current fraud detection methods.
To uncover behavioral pattern differences among money laundering entities.

Main Methods:

Proposed Bit-CHetG, a subgraph-based contrastive learning algorithm for heterogeneous graphs.
Utilized predefined metapaths to construct homogeneous subgraphs from Bitcoin transaction data.
Employed graph neural networks for topological embedding and supervised contrastive learning to mitigate noise.

Main Results:

Bit-CHetG effectively detects money laundering groups by capturing heterogeneity and behavioral patterns.
The algorithm demonstrated improved performance on real-world datasets.
Achieved at least a 5% increase in Micro F1 Score compared to existing methods.

Conclusions:

Subgraph-based contrastive learning is effective for detecting organized money laundering groups.
Bit-CHetG offers a significant advancement in cryptocurrency fraud detection.
The method enhances the ability to identify complex illicit financial networks.