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我们一直在大力投资自动化KYC流程，努力减少人工审核时间并提高入职速度。对于简单案例，我们看到了不错的进展，但边缘案例，特别是涉及复杂公司结构或不常见司法管辖区的政治公众人物（PEPs）的案例，仍然造成了严重的瓶颈。似乎这些情况下的数据聚合和验证仍然是顽固的人力密集型工作。有没有人找到了真正能解决这个问题的技术方案，还是我们只是优化了简单部分，而在真正困难的案例上碰壁了？具体来说，除了更好的OCR和API集成，是否有新兴的AI/ML应用被证明能有效标记甚至解决这些更模糊的风险画像，而不会产生过多的误报？
I hear you on those edge cases. We've found that integrating with more specialized data providers for international corporate registries and PEP lists can sometimes help, but it's often a cost-benefit analysis. Have you looked into any of the newer AI tools for unstructured data analysis on those complex corporate documents?
I hear you on those edge cases. It often feels like the last 5% of complexity takes 50% of the effort, especially with varied international data sources. Are you finding the holdup is more in data collection/validation or in the actual decisioning logic for those trickier profiles?
Your bottleneck sounds like a data problem, not an automation one. If your systems can't reliably source and interpret the nuanced data for complex structures or PEPs in specific regions, no amount of workflow automation will fix that upstream data gap.
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