We have seen a tremendous increase in IT capabilities over the past several decades, as well as an increased availability to the information produced by such systems. This has led to the formation of the science data analytics, alternatively called predictive analytics. This science seeks to uncover actionable insights from the datasets produced by the use of modern technology. The purpose of this paper is to identify how this new phenomena can be implemented in the audit of financial statements with the use of data analysis. The paper looks at relevant regulation in the form of international standards on auditing (ISA’s), to identify any requirements in the use of data analytics. The paper then goes on to examine how exactly an auditor can use data analytics in the audit, while remaining in compliance with current regulation. The purpose of this is to determine whether data analysis, at this stage, can be said to increase the effectiveness of an audit, or if it can merely be classified as additional work the client requires the auditor to perform. In the analysis of relevant regulation pertaining to the audit process, it is clear that there are no specific requirements for the use data analytics. The ISA’s merely seeks to provide a baseline for what is required in an audit, including what is sufficient audit evidence. This means that the use of data analytics in the audit of financial statements will in large part on the auditor’s interpretation of the regulation. However, it was noted in the analysis that IAASB who is responsible for developing the ISA’s are in the process of developing new and revised standards. These revisions include explicit examples of how data analysis among other new technologies can be used in the audit to obtain sufficient audit evidence. The analysis concluded that data analysis could be used in many parts of the audit, including the planning and risk assessment, test of controls, and substantive procedures. The use of data analytics in the risk assessment could lead to a better understanding and identification of risks. For test of controls and substantive procedures, data analytics could, according to Yoshihide Tobas general theory on evidence, be said to generate both confirming and supporting evidence. In many cases this would lead a reduction in otherwise time consuming procedures.
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