@raelyned

Blockchain analytics tools for illicit finance monitoring face high false-positive rates (FPRs), with 30–40% of alerts being benign. Heuristic-based models (e.g., address clustering) generate noisy data, overwhelming compliance teams. Machine learning models reduce FPRs to 15–20% but require labeled training data, which is scarce for emerging threats. Regulatory pressure to flag suspicious activity exacerbates over-reporting. Solutions like contextual analysis (e.g., transaction purpose) and federated learning improve accuracy. However, privacy-preserving techniques (e.g., ZKPs) may limit data availability, creating a trade-off between surveillance and user rights.

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