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How a major commercial property insurer leveraged SemanticPro to globally standardize policies
技术
- 分析与建模 - 机器学习
- 分析与建模 - 自然语言处理 (NLP)
适用功能
- 商业运营
用例
- 欺诈识别
- 监管合规监控
服务
- 数据科学服务
挑战
这家在全球设有办事处的商业财产保险公司拥有约 2,000 名高价值商业客户，每名客户拥有多达 30 份保单。总部制定标准保单，转发给地区办事处并在当地进行调整。由于该公司不使用行业标准表格，当地签发的文件的装订副本在格式和内容上可能与原件不同。审查当地签发的保单的过程是手动完成的，非常耗时。负责这项任务的团队花费大约三分之一的时间寻找源保单和最终版本之间的差异。到目前为止，由于文件类型和格式不同，并且没有工具可以理解语义变化，因此无法实现此审查过程的自动化。然而，该公司寻求一种自动化解决方案，因为大约 70% 的文件在人工审查后仍然包含错误。
关于客户
该客户是一家总部位于美国的主要商业财产保险公司。该公司业务遍布全球，在世界各地设有办事处，为约 2,000 名高价值商业客户提供服务。这些客户中的每一个都有多达 30 份保单，这些保单在公司总部创建，然后转发到地区办事处进行本地调整。该公司不使用行业标准表格，这意味着当地签发的文件的装订副本在格式和内容上可能与原件不同。该公司的团队花费大约三分之一的时间来寻找源保单和最终版本之间的差异，这个过程既需要手动操作，又很耗时。
解决方案
该公司决定利用 SemanticPro Extract & Analyze 自动将标准保单与本地签发的文件进行比较。为了涵盖各种格式和语言，SemanticPro Extract & Analyze 使用了来自不同地区的 100 份文件进行训练，这些文件均来自该公司的主题专家的注释。经过训练后，该解决方案能够逐字逐句地比较保单，并理解同一概念的不同表述。例如，它识别出不可抗力条款中“战争”一词被错误地替换为“冲突”。该解决方案快速准确地报告原始文件和本地签发保单之间所有条款和条件的差异，使公司能够根据紧迫的期限及时进行更正。
运营影响
数量效益
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