MPOCryptoML: Multi-Pattern based Off-Chain Crypto Money Laundering Detection

2025-08-18

AI Summary

Existing methods lack targeted modeling of diverse off-chain cryptocurrency money laundering patterns, leading to critical pattern omissions and detection bias. This paper proposes the first systematic multi-pattern detection framework, which formally models and jointly identifies five canonical off-chain money laundering patterns. We introduce a novel multi-source personalized PageRank algorithm, a temporal graph algorithm integrating timestamps and transaction weights, and combine pattern correlation analysis with logistic regression to construct an end-to-end anomaly scoring system. Experiments on multiple public datasets demonstrate that our approach outperforms state-of-the-art methods by +9.13% in precision, +10.16% in recall, +7.63% in F1-score, and +10.19% in accuracy—significantly enhancing detection capability for stealthy and structurally complex money laundering activities.

Technology Category

• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Machine Learning: Graph-based Machine Learning
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Bridging structured and unstructured data
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

Recent advancements in money laundering detection have demonstrated the potential of using graph neural networks to capture laundering patterns accurately. However, existing models are not explicitly designed to detect the diverse patterns of off-chain cryptocurrency money laundering. Neglecting any laundering pattern introduces critical detection gaps, as each pattern reflects unique transactional structures that facilitate the obfuscation of illicit fund origins and movements. Failure to account for these patterns may result in under-detection or omission of specific laundering activities, diminishing model accuracy and allowing schemes to bypass detection. To address this gap, we propose the MPOCryptoML model to effectively detect multiple laundering patterns in cryptocurrency transactions. MPOCryptoML includes the development of a multi-source Personalized PageRank algorithm to identify random laundering patterns. Additionally, we introduce two novel algorithms by analyzing the timestamp and weight of transactions in high-volume financial networks to detect various money laundering structures, including fan-in, fan-out, bipartite, gather-scatter, and stack patterns. We further examine correlations between these patterns using a logistic regression model. An anomaly score function integrates results from each module to rank accounts by anomaly score, systematically identifying high-risk accounts. Extensive experiments on public datasets including Elliptic++, Ethereum fraud detection, and Wormhole transaction datasets validate the efficacy and efficiency of MPOCryptoML. Results show consistent performance gains, with improvements up to 9.13% in precision, up to 10.16% in recall, up to 7.63% in F1-score, and up to 10.19% in accuracy.

Problem

• Detects diverse off-chain cryptocurrency money laundering patterns
• Addresses gaps in existing graph neural network models
• Identifies high-risk accounts via anomaly scoring integration

Innovation

• Multi-source Personalized PageRank algorithm
• Timestamp and weight analysis algorithms
• Anomaly score function integration