Technology-Assisted Review (TAR) in the crypto context refers to the use of artificial intelligence and machine learning algorithms to expedite and enhance the accuracy of reviewing large volumes of digital data, such as transaction records, communications, or smart contract code. This is particularly relevant for compliance, legal discovery, and forensic investigations in crypto, where manual review of vast, complex, and often pseudonymous data is impractical. TAR streamlines the identification of relevant information and reduces human error.
Mechanism
The mechanism of TAR involves training algorithms on a small, expertly reviewed subset of data to learn patterns indicative of relevance or specific characteristics. The trained model then applies these learnings to the entire dataset, categorizing and prioritizing documents or transactions for human review. Iterative processes, often incorporating active learning, allow the system to refine its understanding based on continuous human feedback. This process efficiently sifts through terabytes of data to surface critical information for compliance or legal teams.
Methodology
Implementing TAR in crypto investigations requires a structured methodology that begins with defining the scope and criteria for relevance. Legal and compliance teams collaborate with data scientists to train and validate the TAR models, ensuring they accurately identify pertinent crypto addresses, transaction types, or communication excerpts. The process involves continuous quality control, typically through random sampling of the system’s output for human validation. This approach significantly reduces the time and cost associated with regulatory inquiries, litigation, or internal audits involving digital assets.
Active learning optimizes reviewer efficiency by transforming linear review into an intelligent dialogue, using human expertise to strategically train a model.
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