A team from China’s national police academy has published a study in the Journal of Intelligence detailing a new AI-driven framework for monitoring cryptocurrency transactions. According to the researchers, led by Dr. Sun Jingchao, the system is designed to combat the rise of money laundering facilitated by the pseudonymous and cross-border nature of digital assets. The study reports that the algorithm can flag suspicious activity with a 90% accuracy rate by analyzing transaction patterns on the blockchain.

While the report focuses on the technical capabilities of the software, it highlights the ongoing challenge law enforcement faces in tracking decentralized finance. The researchers argue that as cryptocurrency adoption grows, traditional investigative methods are becoming insufficient. By applying machine learning to blockchain data, the police aim to bridge the gap between anonymous digital wallets and real-world identities. The study does not specify how the data is being integrated into active police operations, nor does it address potential privacy concerns regarding the mass surveillance of blockchain ledgers. The development reflects a broader global trend of authorities seeking more sophisticated technological tools to regulate and monitor the crypto ecosystem.