To detect fake Tether (USDT) transactions using ETL-enhanced blockchain analysis, extract transaction data from the blockchain using tools like Ethereum ETL, including sender/receiver addresses, amounts, and timestamps. Transform the data by clustering addresses with heuristics (e.g., common spending patterns) and flagging anomalies like unusual volumes or rapid transfers. Integrate off-chain data, such as sanctioned wallet lists, via APIs. Load the processed data into a database for analysis. Use graph analysis to trace fund flows and identify suspicious patterns, like mixer usage or transfers to known illicit exchanges. Machine learning models, trained on historical fraud data, can enhance detection by scoring transactions for risk. This ETL approach ensures scalable, real-time monitoring of Tether transactions for potential fakes.
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