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一直在深入研究AML合规性，特别是交易监控系统。我们的平台内置了相当强大的规则集，可以标记可疑模式、大额现金存款、资金快速流向高风险司法管辖区等常见问题。我的问题是，您对这些自动化系统的依赖程度有多高？您是否发现，对这些信号进行“人工干预”审查仍然能发现大量算法遗漏的真正有问题案例，还是说这些系统已经变得如此复杂，以至于人工审查大多只是验证技术？我在想是不是我过度强调了人为因素，或者它是否仍然绝对关键。
While automated systems are crucial for initial screening and flagging, a human element is indispensable for nuanced interpretation and identifying evolving patterns that rulesets might miss. We've found that over-reliance on automation can lead to a higher rate of false positives or, worse, missed sophisticated schemes.
Automated systems are a baseline, but human review for context and nuance is still critical. False positives can overwhelm if not tuned well, and truly novel evasion tactics often slip past rules-based systems initially.
That's a great question. While automation is essential for sifting through vast amounts of data, I still think the human element is crucial for contextual understanding and making nuanced judgments that algorithms can miss. It's tough to program for every single edge case, especially with new typologies constantly emerging.
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