Money Laundering Detection on Ethereum: Applying Traditional Approaches to New Scene

With the continuous evolution of blockchain technology, cryptocurrency platforms such as Ethereum have emerged as centers for digital asset transactions and smart contracts deployment. However, this nascent financial ecosystem also introduces potential money laundering risks. The traditional financial industry has accumulated significant antimoney laundering (AML) experience and technical means for monitoring and detecting money laundering activities. Yet, the adaptability of these established AML algorithms in the context of blockchain remains unclear. This paper aims to investigate the practical adaptability of traditional AML algorithms on Ethereum data through empirical experiments. We gather eight real-world money laundering case datasets collected from Ethereum and conduct experiments using three traditional AML algorithms on these datasets. We evaluate the performance of these algorithms from various angles, including precision, recall, and the distribution of detected accounts' labels in comparison to the original datasets. It turns out algorithms demonstrate distinct performance in diverse money laundering cases, indicating that the adaptability of traditional AML algorithms on Ethereum data presents certain adaptability and limitations. Holoscope's accuracy demonstrates the value of dense subgraph properties in Ethereum money laundering detection, and further research can be conducted based on this model framework combined with the money laundering characteristics of Ethereum. Our study provides valuable insights for strengthening AML mechanisms on blockchain platforms and offers guidance for further research on detecting money laundering accounts in blockchain environments.