# Characterizing and Detecting Money Laundering Activities on the Bitcoin Network

arXiv: https://arxiv.org/abs/1912.12060 · HTML: https://mlsanity.com/paper/1912.12060/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Dec 2019.

## Built on by (most cited first, 9 of 9)

- [LaundroGraph: Self-Supervised Graph Representation Learning for Anti-Money Laundering](https://mlsanity.com/paper/2210.14360/) (2022)
- [Demystifying Fraudulent Transactions and Illicit Nodes in the Bitcoin Network for Financial Forensics](https://mlsanity.com/paper/2306.06108/) (2023)
- [GuiltyWalker: Distance to illicit nodes in the Bitcoin network](https://mlsanity.com/paper/2102.05373/) (2021)
- [Inspection-L: Self-Supervised GNN Node Embeddings for Money Laundering Detection in Bitcoin](https://mlsanity.com/paper/2203.10465/) (2022)
- [Anti-Money Laundering Alert Optimization Using Machine Learning with Graphs](https://mlsanity.com/paper/2112.07508/) (2021)
- [Detecting Sybil Addresses in Blockchain Airdrops: A Subgraph-based Feature Propagation and Fusion Approach](https://mlsanity.com/paper/2505.09313/) (2025)
- [Label-GCN: An Effective Method for Adding Label Propagation to Graph Convolutional Networks](https://mlsanity.com/paper/2104.02153/) (2021)
- [Functional Classification of Bitcoin Addresses](https://mlsanity.com/paper/2202.12019/) (2022)
- [Detecting Fraud in Financial Networks: A Semi-Supervised GNN Approach with Granger-Causal Explanations](https://mlsanity.com/paper/2507.01980/) (2025)

## Abstract (arXiv)

Bitcoin is by far the most popular crypto-currency solution enabling peer-to-peer payments. Despite some studies highlighting the network does not provide full anonymity, it is still being heavily used for a wide variety of dubious financial activities such as money laundering, ponzi schemes, and ransom-ware payments. In this paper, we explore the landscape of potential money laundering activities occurring across the Bitcoin network. Using data collected over three years, we create transaction graphs and provide an in-depth analysis on various graph characteristics to differentiate money laundering transactions from regular transactions. We found that the main difference between laundering and regular transactions lies in their output values and neighbourhood information. Then, we propose and evaluate a set of classifiers based on four types of graph features: immediate neighbours, curated features, deepwalk embeddings, and node2vec embeddings to classify money laundering and regular transactions. Results show that the node2vec-based classifier outperforms other classifiers in binary classification reaching an average accuracy of 92.29% and an F1-measure of 0.93 and high robustness over a 2.5-year time span. Finally, we demonstrate how effective our classifiers are in discovering unknown laundering services. The classifier performance dropped compared to binary classification, however, the prediction can be improved with simple ensemble techniques for some services.