Chinese University Develops AI for Detecting Illegal Bitcoin Transactions with 89.4% Accuracy
The research team at the Chinese People's Public Security University has developed an AI framework capable of detecting illegal transactions involving virtual currencies, achieving an overall accuracy rate of approximately 89.4%. The model integrates Dynamic Graph Neural Networks (DGNN), memory mechanisms, and Large Language Models (LLM). DGNN analyzes the transaction structure and capital flow within the virtual currency ecosystem, while the memory module compares new transactions with historical illegal transaction patterns. The LLM consolidates the extracted transaction features into natural language risk assessments, providing classifications of legal/illegal status, risk scores, and reasoning chains. The team tested the model on the publicly available Elliptic Bitcoin transaction dataset, which includes 203,769 transaction nodes and 234,355 edges, achieving a precision rate of 89.1% and a recall rate of 64.5% for illegal transaction detection. The related paper was published in May this year in the Journal of Intelligence, stating that this research offers an accurate and interpretable solution for detecting illegal transactions in virtual currencies, potentially becoming an innovative technological pathway for regulatory authorities to combat virtual currency crimes. This research comes at a time when Chinese authorities are intensifying their crackdown on financial crimes involving virtual currencies, with the Supreme People's Procuratorate disclosing that 3,259 individuals were prosecuted last year for allegedly using virtual currencies for money laundering.
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