Contractual obligation extraction using artificial intelligence

使用人工智能提取合同义务

• 批准号: 103023
• 金额: $ 54.75万
• 依托单位: COGNITIV+ LTD
• 依托单位国家: 英国
• 项目类别: Feasibility Studies
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=103023
• 关键词: Contractual; obligation; extraction; using; artificial

Legal Business Contracts govern the business relationship between trading business partners. They are like blueprints of expected business behaviour of all the contracting parties involved, and bind the parties to obligations that must be fulfilled by expected performance events. This highly innovative project proposes the automation of the obligation extraction task using artificial intelligence, especially machine learning and natural language processing. Since the extracted information will be already in machine readable format we also propose the development of software the implements the workflows that have to do with obligation management(e.g.payment calendars, reporting notifications,etc.). The solution, accessed directly or as a service, will help legal, commercial and compliance professionals to accelerate contract review and analysis as well as avoid manual data entry into corporate systems, allowing them to focus on higher-value tasks. It will generate significant cost savings (50-90%) through the reduction of the time spend on manual tasks. On top of that, we expect significant operational risk reduction which will lead to reduced costs of litigation and potential penalties.

法律商业合同规范了贸易伙伴之间的商业关系。它们就像所有相关缔约方预期业务行为的蓝图，并将各方约束于必须通过预期绩效事件履行的义务。这个高度创新的项目提出了使用人工智能，特别是机器学习和自然语言处理来自动化义务提取任务。由于提取的信息已经是机器可读的格式，我们还建议开发软件来实现与义务管理有关的工作流(例如：付款日历、报告通知等)。该解决方案可直接访问或作为服务访问，将帮助法律、商业和合规专业人员加速合同审查和分析，并避免手动将数据输入公司系统，使他们能够专注于更高价值的任务。通过减少花费在手工任务上的时间，它将产生显著的成本节约（50-90%）。最重要的是，我们期望显著降低操作风险，从而降低诉讼成本和潜在的处罚。