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fraud

The United States sanctions Cambodian senator, the crackdown on cryptocurrency fraud continues to escalate

The U.S. Department of the Treasury's Office of Foreign Assets Control (OFAC) announced sanctions against Cambodian Senator Kok An, accusing him of controlling "fraud parks" across the country, defrauding American victims through cryptocurrency investment scams.According to a statement released by OFAC on Thursday, in addition to Kok An, 28 other individuals and entities have been added to the sanctions list, all believed to be connected to his fraud network. This network is accused of luring victims into sending cryptocurrency assets under the guise of "high return investments." This action follows a raid by Cambodian police on two scam centers in the border city of Poipet. Previously, Kok An had been accused of operating scam bases in the area. OFAC stated that scammers typically gain victims' trust by establishing "friendships" or "romantic relationships," then guide them to participate in so-called cryptocurrency investment platforms, thereby defrauding them of funds, with the total amount involved reaching millions of dollars.It is noteworthy that some individuals involved in the scam activities are themselves victims of human trafficking, forced to engage in illegal activities under threats of violence. OFAC pointed out that these scam centers are often located in casinos or repurposed office parks, used not only for money laundering but also as bases for defrauding American citizens and committing human rights violations. Additionally, regulators have simultaneously shut down over 500 fraudulent website domains used for cryptocurrency investment scams, indicating that the U.S. crackdown on related criminal activities is intensifying.

Coinbase upgrades its anti-fraud system, integrating machine learning with a rules engine, reducing response time to a few hours

Coinbase stated that it is optimizing the rule creation process in its anti-fraud system by integrating machine learning models with a rules engine, achieving more efficient risk management. It also proposed a dual-track strategy of "models responsible for long-term defense, rules responsible for rapid response," and built a unified framework to create a feedback loop between the two: rules are used to capture new types of fraud and train the model in reverse, thereby continuously enhancing overall defense capabilities.In terms of specific optimizations, Coinbase has transformed the previously manual rule creation process into a data-driven and automated recommendation system by restructuring data, automating schema evolution, and introducing notebook-based analytical tools, significantly improving efficiency. Among these improvements, the performance of rule backtesting has increased by more than 10 times, and the overall response time has been reduced from several days to a few hours. Additionally, the new system uses machine learning to recommend parameters, helping to reduce false positive rates while combating fraud and minimizing the impact on normal users. Coinbase indicated that the next step will be to advance event-driven automatic rule generation and explore the "one-click conversion" of efficient rules into model features, further moving towards an automated risk management system.
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