ACCT 332 Week 6 Article Review Example

Article Review: How Auditors in Thailand First Put Audit Data Analytics to Work

Article Reviewed

Sanoran, K., and Ruangprapun, J. (2023), "Initial Implementation of Data Analytics and Audit Process Management," published in Sustainability. I chose this article because it asks what auditors actually do with data analytics, a question at the center of my research paper on continuous monitoring and auditing, and because it was published in 2023, within the five-year window.

Summary

Sanoran and Ruangprapun (2023) respond to calls for more evidence on whether big data analytics are being adopted in auditing and whether they work. They ask how auditors use audit data analytic tools across three stages of the audit: planning, testing and forming conclusions. The study is qualitative, based on 28 semi-structured interviews with audit professionals from Big 4 and non-Big 4 firms in Thailand, all describing the early stage of implementing these tools.

The findings are specific. Auditors used analytic tools mainly in planning and in substantive testing, and less in other parts of the engagement. They tended to apply the tools for detecting anomalies and for testing management's assertions, such as the existence and completeness of balances. They did not see a need to use the tools for testing internal controls or for reaching the audit opinion. Overall, the auditors believed the tools improved how they managed the audit process. The authors draw implications for audit firms introducing these tools and for regulators writing standards on the use of big data in audits.

Evaluation

The article's main strength is that it reports practitioners' own experience at a moment when most writing on audit analytics is either promotional or speculative. Organizing the findings by audit stage is also useful, because it shows where adoption has happened and where it has not, rather than treating analytics as a single thing that a firm either has or lacks. Including both Big 4 and smaller firms gives a broader view than a study of one firm's pilot.

The limits are significant, and they matter for how far the findings travel. Twenty-eight interviews in one country cannot show how common these patterns are, and audit regulation and firm practice in Thailand may not match American practice. Interview data also depend on what participants chose to say; auditors might overstate the benefits of tools their firms have invested in. The authors acknowledge both concerns, as well as differences in tools across firms. A further limit is the study's timing: because it describes initial implementation, the finding that auditors see no need for analytics in control testing may reflect early habits rather than a settled judgment. Finally, the benefits the study reports are perceived benefits. The interviews tell us that auditors believe the tools improved their management of the audit, but the study does not measure audit quality, hours or detected misstatements, so it cannot show whether those beliefs are accurate. A follow-up comparing engagements with and without analytics would turn the perceptions into evidence.

Relevance to the Course and My Research

The finding about internal controls is the most relevant to this course. Richardson et al. (2018) treat internal control and the monitoring of systems as central to a reliable AIS, and continuous auditing research argues that automated tests are especially well suited to controls, which are rule-based and repeat thousands of times. Sanoran and Ruangprapun's auditors had not reached that stage. Their experience suggests that adoption starts where analytics replace familiar manual work, such as analytical review in planning, and spreads to control testing only later. Huang et al. (2022) argue that analytics now make it possible to test whole populations rather than samples, which shows how far practice could move beyond the planning-stage uses these auditors described. That pattern will help me explain, in my research paper, why continuous techniques have spread more slowly than their supporters predicted.

Conclusion

This article offers a clear, if limited, picture of how auditors first use data analytics: mainly for planning and substantive tests, and hardly at all for controls. Its small sample and single setting mean its conclusions are best read as patterns to test elsewhere. For a student of accounting information systems, its most useful lesson is that technology enters the audit where it fits existing habits, and that changing those habits is the harder task.

References

Huang, F., No, W. G., Vasarhelyi, M. A., & Yan, Z. (2022). Audit data analytics, machine learning, and full population testing. The Journal of Finance and Data Science, 8, 138-144. https://doi.org/10.1016/j.jfds.2022.05.002

Richardson, V. J., Chang, C. J., & Smith, R. (2018). Accounting information systems (2nd ed.). McGraw-Hill Education.

Sanoran, K., & Ruangprapun, J. (2023). Initial implementation of data analytics and audit process management. Sustainability, 15(3), Article 1766. https://doi.org/10.3390/su15031766