Detecting anomalous cryptocurrency transactions: An AML/CFT application of machine learning-based forensics

Abstract

In shaping the Internet of Money, the application of blockchain and distributed ledger technologies (DLTs) to the financial sector triggered regulatory concerns. Notably, while the user anonymity enabled in this field may safeguard privacy and data protection, the lack of identifiability hinders accountability and challenges the fight against money laundering and the financing of terrorism and proliferation (AML/CFT). As law enforcement agencies and the private sector apply forensics to track crypto transfers across ecosystems that are socio-technical in nature, this paper focuses on the growing relevance of these techniques in a domain where their deployment impacts the traits and evolution of the sphere. In particular, this work offers contextualized insights into the application of methods of machine learning and transaction graph analysis. Namely, it analyzes a real-world dataset of Bitcoin transactions represented as a directed graph network through various techniques. The modeling of blockchain transactions as a complex network suggests that the use of graph-based data analysis methods can help classify transactions and identify illicit ones. Indeed, this work shows that the neural network types known as Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT) are a promising AML/CFT solution. Notably, in this scenario GCN outperform other classic approaches and GAT are applied for the first time to detect anomalies in Bitcoin. Ultimately, the paper upholds the value of public–private synergies to devise forensic strategies conscious of the spirit of explainability and data openness.

A systematic review of anti-money laundering systems literature: Exploring the efficacy of machine learning and deep learning integration

“…Support Vector Machine, are used to improve the accuracy of fraud detection. All these algorithms combined creates a robust framework for attacking money laundering (Pavlidis, 2023;Pocher et al, 2022). Ruchay et al (2023) confirmed the identification of suspicious transactions using the random forest algorithm and demonstrated its effectiveness through an accuracy rate of 99%.…”

“…This reflects the popularity of machine learning in financial data analysis and its ability to Tree is one of the most frequently used algorithms with four appearances, demonstrating its effectiveness in understanding and classifying financial transaction patterns (Alkhalili et al, 2021;Kanamori et al, 2022;Masrom et al, 2023;Ruiz & Angelis, 2022). Furthermore, Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT) appeared three times each, underlining the importance of graph network analysis in suspicious activity detection (Naveed et al, 2023a;Pocher et al, 2022;Song & Gu, 2023). Random Forest, Support Vector Machine, and Gradient Boosted Tree each appeared three times, showing their prevalence and effectiveness in dealing with complex financial data (Alkhalili et al, 2021;Alotibi et al, 2022;Labanca et al, 2022;Masrom et al, 2023;Ruiz & Angelis, 2022;Pocher et al, 2022;Ruchay et al, 2023;Zhang & Trubey, 2019) .…”

“…Furthermore, Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT) appeared three times each, underlining the importance of graph network analysis in suspicious activity detection (Naveed et al, 2023a;Pocher et al, 2022;Song & Gu, 2023). Random Forest, Support Vector Machine, and Gradient Boosted Tree each appeared three times, showing their prevalence and effectiveness in dealing with complex financial data (Alkhalili et al, 2021;Alotibi et al, 2022;Labanca et al, 2022;Masrom et al, 2023;Ruiz & Angelis, 2022;Pocher et al, 2022;Ruchay et al, 2023;Zhang & Trubey, 2019) . Algorithms such as K-Nearest Neighbors, Naive Bayes, Node2Vec, Reinforcement Learning, Deep Neural Network, Isolation Forest, and Deep Protect each appear once, providing diverse tools used to tackle money laundering and financial crime (Alkhalili et al, 2021;Alotibi et al, 2022;Caglayan & Bahtiyar, 2022;Labanca et al, 2022;Lopes et al, 2022;Shahbazi & Byun, 2022).…”

ForensicTransMonitor: A Comprehensive Blockchain Approach to Reinvent Digital Forensics and Evidence Management

“…First is the integration of blockchain technology into cloud forensics, offering methods and architectures that enhance the credibility, effectiveness, and security of forensic investigations in digital environments mostly related to the chain of custody (CoC). The central focus of [3][4][5][6][7][8][9][10][11][12][13][14][15] was the utilization of blockchain technology for ensuring the integrity, traceability, and privacy of digital evidence and CoC in digital forensics. Some of the solutions for specific use cases such as image forensics, healthcare, and finance were generally discussed, as well as blockchain integration with computer forensics in the context of CoC.…”

Cryptocurrency Investigations in Digital Forensics: Contemporary Challenges and Methodological Advances

“…An emergent trend in cryptocurrency forensics has been the leveraging of machine learning algorithms for blockchain data evaluation. These algorithms have exhibited proficiency in unveiling patterns of suspicious activity and forecasting impending deceptive transactions [12,24]. Their prowess extends to handling vast data sets, uncovering correlations potentially elusive to human investigators.…”