Coinbase, Inc.

Alex Reeve

Sid Shekhar

Ming Jiang

Akash Shah

Harrison Dahme

Zhicong Liang

Linwei Chen

Leo Chen

Methods and systems use a second dataset comprising independently validated data based on labeled blockchain operations previously processed through the blockchain network. The use of the second dataset in conjunction with the dataset comprising labeled blockchain characteristics data received from a plurality of sources provides a comprehensive input for a machine learning model to identify and label suspicious blockchain operations. By doing so, the system mitigates fraudulent, criminal, or suspicious transactions. For example, the system may send an alert to a user if a behavior type is deemed a high risk by the machine learning model; the system may furthermore hold the funds associated with the blockchain operation in a separate account while the transaction is manually reviewed. Thus, the system may generate alerts to notify a user of a suspicious behavior type or suspicious blockchain operation, divert funds associated with the transaction, and conduct a manual review of suspicious blockchain operations.

Systems and methods for entity labeling based on behavior

Methods and systems that use of a multi-tiered machine learning architecture that aggregates traits of blockchain and off-chain operations. The multi-tiered machine learning architecture then uses this data to generate recommendations related to account identities for communications (e.g., blockchain operations) that occur across decentralized applications.

Systems and methods for predicting communication account identities across decentralized applications

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San Francisco, CA, United States of America

Alex Reeve