Expansion of analytical methods in auditing education

Abstract

Data analytics is changing the audit environment and carries significant implications for auditing education. Both international auditing education (International Accounting Education Standards Board (IAESB), 2019a; IAESB, 2019b) and U.S.-based regulatory bodies (American Institute of Certified Public Accountants (AICPA), 2021c; AICPA & National Association of State Boards of Accountancy (NASBA), 2021) have made efforts to address the growing expectations for auditing education, citing fraud risk and going concern risk. While auditing courses have progressed to include some computerized audit software for case studies, the study of analytical procedures has been limited to the application of basic financial ratios, trend analyses and common-size financial statements. Demands for advanced analytics place most emphasis on computerized query and computational methods; however, several advanced analytical models, namely the Altman Z-score, Beneish M−score and the Sloan Accrual formula provide opportunities for greater insight on specific audit risks and do not require advanced computer-based skills. The ability to link audit procedures, specifically analytical procedures to the audit objectives of financial risk and going concern risk strengthens the rationale for introduction of these advanced models within the context of auditing education. This paper discusses the inherent value in these analytical models, links them to audit objectives, proposes the inclusion of these three analytical models as a component of auditing education, and suggests that future study be undertaken to assess implementation and student learning. In addition, we recommend future study of other analytical models that may provide further insight for auditing students.