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最近一直在深入研究 DeFi 的合规性方面，有一个问题不断出现：公司到底是如何将链上分析整合到反洗钱风险评分中的？我理解大致的思路，即跟踪钱包、交易模式，但具体来说，你们的粒度有多细？主要是识别已知的非法地址，还是为新的、未标记的钱包建立更复杂的行为模型？你们发现哪些关键数据点最有效，以及如何在不产生大量误报的情况下处理海量数据？对于活跃在这个领域的人来说，你们的经验是什么，哪些平台被证明对这个特定挑战真正有用？
It's a mix, but the trend is definitely towards more complex behavioral models. Identifying known illicit addresses is table stakes, but the real challenge is proactively spotting suspicious activity from unflagged wallets before they become a problem.
That's a great question. I think a lot of firms are still figuring out the right balance, but the trend seems to be towards more complex behavioral models for new wallets, beyond just flagging known illicit addresses. It's a tricky balance between effective AML and not overly burdening legitimate users.
Most are focused on known illicit addresses and direct exposure. Behavioral models are still pretty nascent for new wallets, primarily because the data sets are messy and defining 'risky' behavior without false positives is incredibly hard.
It's definitely a mix. Beyond known addresses, many are developing heuristics for suspicious activity, like rapid transfers to multiple new wallets or large, unexplained inbound flows from mixers. The challenge is balancing accuracy with false positives.
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