技术
- 分析与建模 - 机器学习
- 传感器 - 液体检测传感器
适用行业
- 教育
- 金融与保险
适用功能
- 质量保证
用例
- 时间敏感网络
- 虚拟培训
服务
- 系统集成
- 培训
关于客户
本案例研究中的客户是一家全球托管银行。作为一家金融机构,根据政府政策,它有义务进行客户尽职调查,作为客户入职的一部分。这包括识别和验证法人实体客户的受益所有人的身份,以遵守反洗钱 (AML) 和反恐怖主义融资 (ATF) 法律法规。作为 KYC 流程的一部分,该银行每年处理 10,000 多份文件,数百名分析师花费大量时间审查和抄录 10-K。该银行正在寻找一种解决方案,可以自动化该流程、节省时间并确保遵守不断变化的法规。
挑战
一家全球托管银行在了解您的客户 (KYC) 流程中面临重大挑战。分析师和投资经理每年花费 10,000 多个小时审查和抄录 10-K,这对于验证公司身份、建立风险状况以及为多个业务流程提供信息至关重要。该银行每年处理 10,000 多份文件,每份文件的审核时间为 30 至 90 分钟。由于 10-K 的格式多种多样,这一过程变得更加复杂,如果有任何信息丢失或不正确,分析师就必须花费额外的时间来查找它。这不仅延长了客户入职流程,还给竞争对手提供了趁虚而入的机会。该银行曾尝试使用基于规则的系统来解决该问题,但事实证明该系统过于僵化,只能识别有限范围的信息。文档格式/布局。由于多个地区的法规不断变化,该系统还需要频繁更新,这需要几个月的时间才能实施。
解决方案
该银行求助于 Snorkel Flow 来构建一个以数据为中心的人工智能应用程序,该应用程序可以自动化 KYC 流程。该银行的数据科学家、数据工程师、机器学习工程师和主题专家 (SME) 合作构建了一个基于 AI 的解决方案,为团队节省了 10,000 多个小时,并节省了与手动标记数据相关的数十万美元的成本。该团队仅用 25 小时的 SME 时间就在风险状况方面获得了 +86 F1 宏观分数。在几周内,他们创建了一个 AI 应用程序,可以获取 10-K 的 PDF,并从表格、原始文本和多页 PDF 文档中提取 50 多个不同的属性,例如业务性质、位置、关键高级管理人员等。该应用程序还对提取的实体进行分类并执行文档级聚合,然后将所有数据输出为结构化表格格式以进行下游高级分析。该团队能够通过快速代码编辑来确保对标记功能的适应性,扩展将复杂的特定领域文本标记为训练数据的能力,并改善领域专家和数据科学家之间在标记、故障排除和迭代方面的协作。
运营影响
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