Contractual obligation extraction using artificial intelligence

合法的商业合同管理贸易伙伴之间的商业关系。它们就像所有涉及的签约各方的预期商业行为的蓝图，并将各方约束于必须通过预期业绩事件履行的义务。这一极具创新性的项目建议使用人工智能，特别是机器学习和自然语言处理，实现义务提取任务的自动化。由于提取的信息将已经是机器可读的格式，我们还建议开发软件，以实现与债务管理有关的工作流程(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快合同审查和分析，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。通过减少人工任务所花费的时间，将产生显著的成本节约(50%-90%)。最重要的是，我们预计运营风险将大幅降低，这将导致诉讼成本和可能的罚款减少。

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.