航空公司利用人工智能简化发票处理
技术
- 分析与建模 - 机器学习
- 网络与连接 - 射频识别
适用行业
- 设备与机械
- 运输
用例
- 时间敏感网络
- 交通监控
服务
- 数据科学服务
- 系统集成
关于客户
该客户是东亚领先的低成本航空公司之一，提供中长途航线。该航空公司致力于让所有人都能享受旅行和所提供的新体验。随着航空公司不断扩张，增加新的目的地，供应商和需要处理的发票数量也在增加。该航空公司需要一种解决方案来处理不断增加的发票数量和可变性，并简化其 BPM 系统中的数据提取和处理。
挑战
该客户是东亚一家领先的低成本航空公司，每月要处理大约 2,000 张发票，每张发票长度为 100 到 200 页不等。这些发票来自一千多家供应商，格式多种多样。手动从每张发票中提取数据并将其处理到航空公司的 BPM 系统中的任务是重复且耗时的。新目的地的增加只会增加供应商和发票的数量，使流程进一步复杂化。该航空公司现有的 OCR 平台因发票格式的变化而苦苦挣扎，需要不断的干预。这些文档的频繁更改使得基于模板的自动化方法变得困难。该公司需要一种基于人工智能且不需要数据科学专业知识的解决方案。
解决方案
该航空公司采用了人工智能、机器学习和模式识别相结合的认知机器阅读 (CMR)。 CMR 从服务票务工具 Service Now 捕获发票，并模拟人工数据处理方法来提取关键数据元素。它对发票进行分类并为下游系统提供精选数据。内置工作流程根据业务规则将数据路由到指定系统。 CMR 自动将丢失或不可读的信息标记为例外情况，并将任务分配给正确的人员。每次手动干预都会提高系统的准确性。多个报告可用于进一步分析，为航空公司的发票处理挑战提供全面的解决方案。
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