Demonstrating The Payroll Reviews Based On Data Visualization For Financial Services

Abstract

This study proposes a framework for proactive and intelligent continuous control monitoring (CCM), which can help executives feel more secure in their company's operations while also easing the stress of being flooded with data. The framework was developed by the Continuous Control Monitoring Consortium (CCMC). The development of CCM artifacts, such as the display of operational and internal control violations and the identification of multidimensional abnormalities, is approached from a design science perspective by our team. In order to demonstrate the architecture, we will give a case study of a real-world implementation. This implementation will involve a company that provides accounting services, a client in the healthcare industry, and the research team all working together to improve the dependability of payroll audits. This paper makes a contribution to the CCM literature by proposing that the utilization of machine learning and interactive data visualization can assist relieve the problem of managers having access to an excessive amount of data. Following this, we present evidence that, from both an economic and a behavioral point of view, the control monitoring approach is an improvement over the traditional one. We demonstrate how cutting-edge technology improves risk assessment, the identification of anomalies, and the avoidance of loss both more efficient and accurate. In addition, we provide guidelines for the production and utilization of artifacts, which is another way that we contribute to the field of control monitoring.