一家大型银行如何通过自动化信用协议分析节省数千小时的人工劳动
公司规模
Large Corporate
地区
- America
国家
- United States
产品
- Cortical.io SemanticPro
技术栈
- Natural Language Processing
实施规模
- Enterprise-wide Deployment
影响指标
- Productivity Improvements
- Cost Savings
- Digital Expertise
技术
- 分析与建模 - 自然语言处理 (NLP)
适用行业
- 金融与保险
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
关于客户
客户是美国的一家一级银行。该银行正在探索通过自动化提高效率的方法。银行的目标是找到一种合适的提取工具来帮助实现风险评估流程的自动化，并在结果质量足够可靠的情况下降低错误契约评估的运营风险。该银行的战略与新兴技术副总裁 Martha 负责确定自动化可以提高组织流程效率的领域。她不断探索新技术以跟上市场的发展，并与业务线所有者进行内部访谈以评估优化潜力。确定需要改进的领域之一是批发信贷部门，由 Rick 领导，他的团队花费大量时间彻底审查财务信息以评估信贷风险。
挑战
该银行正在寻找通过自动化提高效率的方法，特别是在信用风险评估领域。批发信贷协议的语言和结构复杂性使得自动化变得困难。这些协议非常复杂，有 100 多页的文档，而且非常定制化，因为银行使用不同的贷款系统。特别是契约使用高度具体的用例相关公式。一些提取很简单，可以“按原样”提取值（无需推理），例如：指定日期、承诺、利率、当事人。其他则更具挑战性，需要在选项或契约之间进行细粒度区分（例如赎回交易选项、终止选项）。在这里，自动化系统必须根据文档结构和提取项目的关系来理解域逻辑。与定价相关的表格包含许多不同的参数（适用保证金、利息支付计划、承诺表），并且很难自动提取定价信息。这就是为什么信贷协议的审查仍然是手动完成的，这给银行带来了大量时间和金钱，更不用说这种平凡任务的高错误率了。
解决方案
该银行利用 Cortical.io SemanticPro 基于含义的功能从信用协议中提取关键信息并自动对契约进行分类。在研究了市场上的不同解决方案后，Martha 决定测试 Cortical.io 的自然语言处理功能并将其应用于信用协议的分析。她与 Cortical.io 合作启动项目的主要原因是该系统可以轻松快速地适应 Rick 的使用案例：只需要有限数量的信用协议示例（少于 50 个）即可训练系统。这极大地限制了该项目的初始投资和总体风险。在初始基础知识阶段之后，系统以无人监督的方式从相关源材料中学习，Rick 团队的两名贷款人员通过审查一小部分文件提供了输入。此后，系统不断吸收反馈并创建更准确的模型（持续学习，又称人机循环）。
运营影响
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