Continuous auditing

Abstract: The advent of new enabling technologies and the surge in corporate scandals has combined to increase the supply, the demand, and the development of enabling technologies for a new system of continuous assurance and measurement. This paper positions continuous assurance (CA) as a methodology for the analytic monitoring of corporate business processes, taking advantage of the automation and integration of business processes brought about by information technologies. Continuous analytic monitoring‐based assurance will change the objectives, timing, processes, tools, and outcomes of the assurance process. The objectives of assurance will expand to encompass a wide set of qualitative and quantitative management reports. The nature of this assurance will be closer to supervisory activities and will involve intensive interchange with more of the firm's stakeholders than just its shareholders. The timing of the audit process will be very close to the event, automated, and will conform to the natural life cycle of...

Abstract: In the almost twenty years since Vasarhelyi and Halper (1991) reported on their pioneering implementation of what has come to be known as Continuous Auditing (CA), the concept has increasingly moved from theory into practice. A 2006 survey by PricewaterhouseCoopers shows that half of all responding firms use some sort of CA techniques, and the majority of the rest plan to do so in the near future. CA not only has an increasing impact on auditing practice, but is also one of the rare instances in which such a significant change was led by the researchers. In this paper we survey the state of CA after two decades of research into continuous auditing theory and practice, and draw out the lessons learned by us in recent pilot CA projects at two major firms, to examine where this unique partnership between academics and auditors will take CA in the future.

Abstract: SYNOPSIS: Big Data now pervades every sector and function of the global economy. This paper focuses on the gaps between Big Data and the current capabilities of data analysis in continuous auditing (CA). It identifies four dimensions of Big Data and five subsequent gaps: namely, data consistency, integrity, aggregation, identification, and confidentiality. For each gap, the paper outlines challenges and possible solutions derived from traditional data systems, which can be further applied to CA systems in an era of Big Data.