# Detecting Anomalous Cryptocurrency Transactions: an AML/CFT Application of Machine Learning-based Forensics

arXiv: https://arxiv.org/abs/2206.04803 · HTML: https://mlsanity.com/paper/2206.04803/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 97 references that are in the map.
- First posted on arXiv 7 Jun 2022.

## Builds on (most cited first, 18 of 18)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://mlsanity.com/paper/1606.09375/) (2016)
- [Design Space for Graph Neural Networks](https://mlsanity.com/paper/2011.08843/) (2020)
- [Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensics](https://mlsanity.com/paper/1908.02591/) (2019)
- [Scalable Graph Learning for Anti-Money Laundering: A First Look](https://mlsanity.com/paper/1812.00076/) (2018)
- [Detecting Mixing Services via Mining Bitcoin Transaction Network with Hybrid Motifs](https://mlsanity.com/paper/2001.05233/) (2020)
- [Machine learning methods to detect money laundering in the Bitcoin blockchain in the presence of label scarcity](https://mlsanity.com/paper/2005.14635/) (2020)
- [Dissecting Ponzi schemes on Ethereum: identification, analysis, and impact](https://mlsanity.com/paper/1703.03779/) (2017)
- [Multi-head or Single-head? An Empirical Comparison for Transformer Training](https://mlsanity.com/paper/2106.09650/) (2021)
- [GuiltyWalker: Distance to illicit nodes in the Bitcoin network](https://mlsanity.com/paper/2102.05373/) (2021)
- [The Unreasonable Effectiveness of Address Clustering](https://mlsanity.com/paper/1605.06369/) (2016)
- [Anti-Money Laundering Alert Optimization Using Machine Learning with Graphs](https://mlsanity.com/paper/2112.07508/) (2021)
- [Bitcoin Transaction Graph Analysis](https://mlsanity.com/paper/1502.01657/) (2015)
- [On the Ethereum Blockchain Structure: a Complex Networks Theory Perspective](https://mlsanity.com/paper/1908.11808/) (2019)
- [CubeFlow: Money Laundering Detection with Coupled Tensors](https://mlsanity.com/paper/2103.12411/) (2021)
- [Cryptocurrencies Activity as a Complex Network: Analysis of Transactions Graphs](https://mlsanity.com/paper/2110.14765/) (2021)
- [DeFi-ning DeFi: Challenges & Pathway](https://mlsanity.com/paper/2101.05589/) (2021)

## Built on by (most cited first, 5 of 5)

- [Machine Learning for Blockchain Data Analysis: Progress and Opportunities](https://mlsanity.com/paper/2404.18251/) (2024)
- [Machine Learning on Blockchain Data: A Systematic Mapping Study](https://mlsanity.com/paper/2403.17081/) (2024)
- [Investigating Similarities Across Decentralized Financial (DeFi) Services](https://mlsanity.com/paper/2404.00034/) (2024)
- [Inference-friendly Graph Compression for Graph Neural Networks](https://mlsanity.com/paper/2504.13034/) (2025)
- [Robust Spectral Fuzzy Clustering of Multivariate Time Series with Applications to Electroencephalogram](https://mlsanity.com/paper/2506.22861/) (2025)

## Abstract (arXiv)

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.