AISmartCorps: AI based smart assistance for enterprise workforces

金融服务公司需要获取大量的数据，这些数据需要检查和解决错误。这是一套耗时且往往过于手动的任务，使工作人员承受巨大压力，可能导致资源配置挑战和业务风险增加。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，必须选择将精力集中在哪里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，从而减轻了员工的沉重负担，使他们能够专注于公司更有价值和更有技巧的任务。我们将根据员工的技能、知识、偏好、可用性和健康指标，开发错误检测、解决方案建议以及“智能”任务分配的算法。这不仅会通过更好地管理工作量，同时提供更有刺激性和满意度的工作，提高员工的满意度，它还将提高公司效率，减少可能导致业务风险增加的错误传播。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）数据密集型行业，在这些行业中，异常检测和分类非常重要。

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.