Chainalysis Redefines the Role of Machine Learning

Chainalysis has stated that machine learning should be used as an auxiliary tool in blockchain intelligence. Address clustering and entity attribution should be treated as reproducible and auditable evidence standards. In a post released on the 14th, it warned that accepting the outputs of automated tools without verification could lead to incorrect address clustering and subsequent harm. Machine learning is not used for wallet segment identification and is selectively applied only in areas such as lead generation, anomaly detection, pattern recognition, and scam detection. Chainalysis distinguishes blockchain address analysis into structural judgments, attribute assignment, and operator identification, emphasizing that a deterministic and reproducible standard is necessary to determine whether multiple addresses are under the same control. It added that machine learning is not included in structural claims and cannot replace the conclusion that addresses are controlled by the same entity. This discussion also impacts the domestic virtual asset market, as inaccuracies in address clustering and entity attribution could classify normal users as high-risk targets. Independent research compared Chainalysis's analytical results with actual address data, presenting an accuracy rate of 94.85%, up from 24.54%. Chainalysis emphasizes the accuracy of its data and low false positive rates, linking it to legal reliability. Public response has been limited, and industry insiders have expressed that clusters should not be equated with actual organizations.

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