AI-Based Framework for Classifying Cryptocurrency Exchanges through Transaction Feature Analysis

Money laundering through cryptocurrencies has been increasing in recent years. To prevent the misuse of virtual assets for money laundering, we perform exchange classification. In this paper, we introduce an AI-based framework that automatically classifies cryptocurrency exchanges by utilizing wallet addresses and transaction data. Specifically, we collected approximately 2 million wallet addresses and transaction data for 50 exchanges. To address the limitations of single models and maximize classification performance, we applied Graph AI models, including the Heterogeneous Graph Attention Network and the Relational Graph Convolutional Network, in combination with ensemble techniques. As a result, our proposed frameworks achieved an average F1-score of 0.8676 across the 50 exchanges. Furthermore, this framework significantly improves the accuracy of tracking wallets involved in cryptocurrency crimes and fosters stronger cooperation with exchanges. The key contributions of this study include the following: 1) Tackling the understudied problem of exchange classification for anti-money laundering, 2) Developing a high-quality labeled dataset of exchange wallet addresses and transactions, and 3) Designing an effective feature set and graph AI approach for exchange classification based on transaction pattern analysis. Ultimately, our extensive experimental results demonstrate the potential for improving cryptocurrency crime detection and recovery through advanced AI techniques.