This paper presents a framework for proactive and intelligent continuous control monitoring (CCM) that helps management gain higher assurance over business processes and alleviate information overload. We adopt a design science approach towards systematically developing CCM artifacts, including operation and internal control violation display and multidimensional anomaly detection. We illustrate the design with an implementation project whereby a CPA firm, the firm's healthcare sector client, and the research team work together to improve the assurance provided by payroll reviews. This study contributes to the CCM literature by envisioning that interactive data visualization and machine learning technologies can alleviate information overload for management in CCM. Second, we provide real-world evidence on the improvement brought to economic and behavioral aspects of the control monitoring process compared to the traditional approach. We show that emerging technologies substantially improve the efficiency and effectiveness of risk assessment, anomaly detection, and loss prevention. We also contribute to control monitoring practice by providing guidance on artifact development and application for practitioners to follow.

本文提出了一个主动和智能的持续控制监控 (CCM) 框架，可帮助管理层对业务流程获得更高的保证并减轻信息过载。我们采用设计科学的方法来系统地开发 CCM 工件，包括操作和内部控制违规显示和多维异常检测。我们通过一个实施项目来说明该设计，在该项目中，一家 CPA 公司、该公司的医疗保健部门客户和研究团队共同合作，以提高工资单审查提供的保证。本研究通过设想交互式数据可视化和机器学习技术可以减轻 CCM 管理中的信息过载，为 CCM 文献做出贡献。第二，我们提供了真实世界的证据，证明与传统方法相比，控制监控过程在经济和行为方面的改进。我们表明，新兴技术大大提高了风险评估、异常检测和损失预防的效率和有效性。我们还通过为从业者提供有关工件开发和应用的指导来为控制监控实践做出贡献。