AISmartCorps: AI based smart assistance for enterprise workforces

AISmartCorps：基于人工智能的企业员工智能辅助

• 批准号: 10004727
• 金额: $ 79.81万
• 依托单位: ONCORPS LIMITED
• 依托单位国家: 英国
• 项目类别: Collaborative R&D
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=10004727
• 关键词: AISmartCorps; AI; based; smart; assistance

Financial service companies acquire huge volumes of data which require checking for, and resolving, errors. This is a time consuming and often overly manual set of tasks that put staff under significant pressures which can lead to resourcing challenges and increased business risks. In addition, as data volumes increase, companies are finding it harder and harder to effectively check every line of data and have to choose where to focus their efforts to minimise risks.We aim to develop and deploy state-of-the-art AI algorithms for automating error detection and recommendations for appropriate resolutions, thus reducing this significant burden on staff and allowing them to focus on more valuable and skillful tasks for the company. We will develop algorithms for error detection, resolution recommendation as well as 'smart' task allocation based on staff skills, knowledge, preferences, availability and wellbeing indicators.Not only will this increase staff satisfaction by better managing workloads whilst providing more stimulating and satisfying work, it will also increase company efficiency and reduce the propagation of errors that can lead to increased business risks.Whilst financial services is our starting point, the algorithms, techniques and platform we will develop are expected to be generally applicable across most if not all data intensive industries where anomaly detection and triage is important.

金融服务公司需要获取大量数据，这些数据需要检查和解决错误。这是一组耗时且通常过于手动的任务，使员工承受巨大压力，从而导致资源挑战和增加业务风险。此外，随着数据量的增加，企业发现越来越难以有效地检查每一行数据，并且必须选择将精力集中在何处以最大限度地降低风险。我们的目标是开发和部署最先进的人工智能算法，用于自动化错误检测和建议适当的解决方案，从而减轻员工的这一重大负担，使他们能够专注于公司更有价值和更熟练的任务。我们将根据员工技能、知识、偏好、可用性和福利指标，开发错误检测、解决方案建议以及“智能”任务分配的算法。这不仅可以通过更好地管理工作量来提高员工满意度，同时提供更令人兴奋和满意的工作，还可以提高公司效率，减少可能导致业务风险增加的错误传播。虽然金融服务是我们的起点，但我们将开发的算法、技术和平台预计将普遍适用于大多数（如果不是所有的话）数据密集型行业，因为异常检测和分类非常重要。