Analyzing the Role of Intelligent Accounting in Improving Internal Controls, Reducing Human Error, and Accelerating Financial Information Processing
Keywords:
Intelligent accounting, Internal control, Human error, Financial information processing, Artificial intelligence, Thematic analysis.Abstract
This study aimed to analyze the role of intelligent accounting in improving internal controls, reducing human error, and accelerating financial information processing in organizations located in Tehran. This qualitative study was conducted using thematic analysis. The participants included 18 financial managers, senior accountants, internal auditors, accounting information system specialists, and consultants involved in the implementation of intelligent financial software in Tehran. Participants were selected through purposive sampling, and recruitment continued until theoretical saturation was achieved. Data were collected through semi-structured interviews. All interviews were conducted after obtaining informed consent, audio-recorded, transcribed verbatim, and coded using NVivo software. Data analysis followed the main phases of thematic analysis, including familiarization with the data, generation of initial codes, development of candidate themes, review and refinement of themes, naming of final categories, and reporting of findings. To enhance trustworthiness, member checking, peer review, audit trail documentation, and constant comparison of codes were applied. The analysis resulted in four main categories: “strengthening internal controls through intelligent monitoring,” “reducing human error through automation and standardization,” “accelerating and integrating financial information processing,” and “the need for data governance and human oversight in intelligent accounting.” Participants emphasized that intelligent accounting systems improve financial control quality through access controls, automated alerts, change tracking, anomaly detection, reconciliation support, and real-time processing of accounting data. However, excessive dependence on technology, poor input data quality, weak documentation, and the absence of clear control policies were identified as major implementation challenges. Intelligent accounting can transform internal control mechanisms from traditional, retrospective, and manual procedures into preventive, analytical, and real-time control systems. This transformation may reduce human error, increase the speed of financial information processing, and enhance the reliability of financial reporting, provided that it is accompanied by data governance, complementary controls, staff training, and professional human judgment.
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References
Alles, M. G. (2015). Drivers of the use and facilitators and obstacles of the evolution of Big Data by the audit profession. Accounting Horizons, 29(2), 439–449. https://doi.org/10.2308/acch-51067
Alles, M. G., Kogan, A., & Vasarhelyi, M. A. (2008). Putting continuous auditing theory into practice: Lessons from two pilot implementations. Journal of Information Systems, 22(2), 195–214. https://doi.org/10.2308/jis.2008.22.2.195
Appelbaum, D., Kogan, A., & Vasarhelyi, M. A. (2017). Big Data and analytics in the modern audit engagement: Research needs. Auditing: A Journal of Practice & Theory, 36(4), 1–27. https://doi.org/10.2308/ajpt-51684
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa
Committee of Sponsoring Organizations of the Treadway Commission. (2013). Internal Control—Integrated Framework: Executive Summary. COSO.
Cooper, L. A., Holderness, D. K., Sorensen, T. L., & Wood, D. A. (2019). Robotic process automation in public accounting. Accounting Horizons, 33(4), 15–35. https://doi.org/10.2308/acch-52466
DeLone, W. H., & McLean, E. R. (2003). The DeLone and McLean model of information systems success: A ten-year update. Journal of Management Information Systems, 19(4), 9–30. https://doi.org/10.1080/07421222.2003.11045748
Eulerich, M., Pawlowski, J., Waddoups, N. J., & Wood, D. A. (2022). A framework for using robotic process automation for audit tasks. Contemporary Accounting Research, 39(1), 691–720. https://doi.org/10.1111/1911-3846.12723
Guest, G., Bunce, A., & Johnson, L. (2006). How many interviews are enough? An experiment with data saturation and variability. Field Methods, 18(1), 59–82. https://doi.org/10.1177/1525822X05279903
Issa, H., Sun, T., & Vasarhelyi, M. A. (2016). Research ideas for artificial intelligence in auditing: The formalization of audit and workforce supplementation. Journal of Emerging Technologies in Accounting, 13(2), 1–20. https://doi.org/10.2308/jeta-10511
Kokina, J., & Davenport, T. H. (2017). The emergence of artificial intelligence: How automation is changing auditing. Journal of Emerging Technologies in Accounting, 14(1), 115–122. https://doi.org/10.2308/jeta-51730
Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic Inquiry. Sage.
Moffitt, K. C., Rozario, A. M., & Vasarhelyi, M. A. (2018). Robotic process automation for auditing. Journal of Emerging Technologies in Accounting, 15(1), 1–10. https://doi.org/10.2308/jeta-10589
Nowell, L. S., Norris, J. M., White, D. E., & Moules, N. J. (2017). Thematic analysis: Striving to meet the trustworthiness criteria. International Journal of Qualitative Methods, 16(1), 1–13. https://doi.org/10.1177/1609406917733847
Vasarhelyi, M. A., Alles, M. G., & Kogan, A. (2004). Principles of analytic monitoring for continuous assurance. Journal of Emerging Technologies in Accounting, 1(1), 1–21. https://doi.org/10.2308/jeta.2004.1.1.1
Vasarhelyi, M. A., Kogan, A., & Tuttle, B. M. (2015). Big Data in accounting: An overview. Accounting Horizons, 29(2), 381–396. https://doi.org/10.2308/acch-51071
