Main Article Content
Abstract
The study explores contemporary challenges and innovative solutions in audit practice, aiming to enhance audit quality and effectiveness in the digital age. The purpose is to examine the multifaceted nature of the audit profession and its dynamic interaction with evolving business landscapes, regulatory frameworks, and technological advancements. Employing a literature review approach, the research design and methodology involve synthesizing insights from scholarly articles and theoretical frameworks to elucidate key themes and trends in audit practice. Findings reveal significant hurdles faced by auditors, including the escalating complexity of financial transactions, regulatory compliance requirements, and the emergence of new technologies. Moreover, innovative solutions such as data analytics, artificial intelligence, continuous auditing, and interdisciplinary collaboration offer promising avenues for addressing these challenges and providing stakeholders with timely assurance and insights into organizational performance. The implications of the study underscore the importance of proactive adaptation, continuous professional development, and collaboration among audit professionals, academia, industry stakeholders, and regulatory bodies in driving innovation, enhancing knowledge sharing, and advancing best practices in the audit profession. The study contributes to the literature by providing insights into emerging audit trends, best practices, and challenges facing the profession, with implications for audit quality, efficiency, and effectiveness in the digital age
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References
- Abbott, L. J., Parker, S., & Peters, G. F. (2010). Audit committee characteristics and restatements. Auditing: A Journal of Practice & Theory, 29(1), 241–255. https://doi.org/10.2308/aud.2010.29.1.241
- Abbott, L. J., Parker, S., & Peters, G. F. (2010). Auditors' assessment of financial reporting quality and the information environment. Auditing: A Journal of Practice & Theory, 29(1), 23-51. https://doi.org/10.2308/aud.2010.29.1.23
- Abbott, L. J., Parker, S., & Peters, G. F. (2021). Audit committee characteristics and restatements. Auditing: A Journal of Practice & Theory, 29(1), 241–255. https://doi.org/10.2308/aud.2010.29.1.241
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- Bédard, J., & Gendron, Y. (2010). Strengthening the financial reporting system: Can audit quality be an answer? Accounting and Business Research, 40(3), 279–293. https://doi.org/10.1080/00014788.2010.9663340
- Braun, L., Luippold, B., & Schmidt, M. (2017). Artificial intelligence in auditing: Opportunities and challenges. Journal of Information Systems, 34(3), 139–155. https://doi.org/10.2308/isys-52540
- Chan, A. S., Lee, C. Y., Lo, W. C., & Zhang, J. (2021). Machine learning in auditing: Opportunities, challenges, and implications for audit quality. Journal of Accounting Research, 59(5), 1439–1486. https://doi.org/10.1111/1475-679X.12428
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- Huang, C. S., Lin, K. J., Wang, K. H., & Wei, K. Y. (2020). Data analytics in auditing: Opportunities and challenges. Journal of Information Systems, 34(3), 139–155. https://doi.org/10.2308/isys-52540
- Hutchins, M., & Swanson, Z. L. (2021). Integrating blockchain technology into accounting and auditing curricula. Journal of Accounting Education, 57, 100747. https://doi.org/10.1016/j.jaccedu.2021.100747
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- IIA. (2021). International Standards for the Professional Practice of Internal Auditing. Institute of Internal Auditors.
- Jiang, J., & Zhang, W. (2019). Data-driven auditing: Leveraging big data for audit planning and execution. Auditing: A Journal of Practice & Theory, 39(4), 169–188. https://doi.org/10.2308/ajpt-52605
- Johnson, J. (2003). The need for changes in audit education. Journal of Accounting Education, 21(1), 1-16. https://doi.org/10.1016/S0748-5751(02)00058-3
- Kapula, J. M. (2015). Challenges faced by internal auditors in the corporate sector. International Journal of Auditing, 19(2), 87-102. https://doi.org/10.1111/ijau.12042
- Knechel, W. R., & Salterio, S. E. (2016). Auditing: Assurance and risk (4th ed.). Routledge.
- Lobo, G. J., Rajan, R., & Selvam, M. (2020). A systematic review of artificial intelligence in auditing: Where are we and where should we be going? Journal of Information Systems, 34(1), 1–29. https://doi.org/10.2308/isys-52454
- PCAOB. (2021). 2021 inspection observations. Public Company Accounting Oversight Board. https://doi.org/10.1177/00018392211057607
- Peursem, K. A. (2005). Pressures on auditors to compromise independence. The European Accounting Review, 14(2), 339-368. https://doi.org/10.1080/09638180500042928
- Pflugrath, G., Martinov-Bennie, N., & Chen, L. (2007). The impact of the Sarbanes-Oxley Act on the corporate governance role of internal audit: Evidence from Australia. Accounting & Finance, 47(4), 717–744. https://doi.org/10.1111/j.1467-629X.2007.00236.x
- Power, M. (2016). The audit society: Rituals of verification. Oxford University Press.
- Power, M. (2020). Auditing and the production of legitimacy. Journal of Management Studies, 57(7), 1325–1350. https://doi.org/10.1111/joms.12575
- Spathis, C., & Doumpos, M. (2014). Data envelopment analysis, clustering, and non-parametric classification techniques for risk evaluation: An integrated approach. Omega, 44, 53–67. https://doi.org/10.1016/j.omega.2013.11.001
- Vasarhelyi, M. A., & Halper, F. (2014). Continuous monitoring: A new paradigm for audit? Auditing: A Journal of Practice & Theory, 33(1), 231–260. https://doi.org/10.2308/ajpt-50611
- Wen, Y., Wu, S., Chen, X., & Lu, Y. (2020). The effects of Big Data analytics and artificial intelligence on audit procedures: An experimental investigation. Auditing: A Journal of Practice & Theory, 39(4), 169–188. https://doi.org/10.2308/ajpt-52605
References
Abbott, L. J., Parker, S., & Peters, G. F. (2010). Audit committee characteristics and restatements. Auditing: A Journal of Practice & Theory, 29(1), 241–255. https://doi.org/10.2308/aud.2010.29.1.241
Abbott, L. J., Parker, S., & Peters, G. F. (2010). Auditors' assessment of financial reporting quality and the information environment. Auditing: A Journal of Practice & Theory, 29(1), 23-51. https://doi.org/10.2308/aud.2010.29.1.23
Abbott, L. J., Parker, S., & Peters, G. F. (2021). Audit committee characteristics and restatements. Auditing: A Journal of Practice & Theory, 29(1), 241–255. https://doi.org/10.2308/aud.2010.29.1.241
Bédard, J., & Gendron, Y. (2010). Strengthening the financial reporting system: Can audit quality be an answer? Accounting and Business Research, 40(3), 279–293. https://doi.org/10.1080/00014788.2010.9663340
Bédard, J., & Gendron, Y. (2010). Strengthening the financial reporting system: Can audit quality be an answer? Accounting and Business Research, 40(3), 279–293. https://doi.org/10.1080/00014788.2010.9663340
Braun, L., Luippold, B., & Schmidt, M. (2017). Artificial intelligence in auditing: Opportunities and challenges. Journal of Information Systems, 34(3), 139–155. https://doi.org/10.2308/isys-52540
Chan, A. S., Lee, C. Y., Lo, W. C., & Zhang, J. (2021). Machine learning in auditing: Opportunities, challenges, and implications for audit quality. Journal of Accounting Research, 59(5), 1439–1486. https://doi.org/10.1111/1475-679X.12428
Chen, C. J. P., Ding, Y., Cheng, L. T. W., & Yang, Y. W. (2021). Auditors’ judgment in detecting financial statement fraud in multinational corporations. Auditing: A Journal of Practice & Theory, 40(3), 69–97. https://doi.org/10.2308/ajpt-52933
Deumes, R., & Knechel, W. R. (2008). The allocation of internal audit resources: An explanation of the determinants of internal audit across industry sectors. Auditing: A Journal of Practice & Theory, 27(1), 63–90. https://doi.org/10.2308/aud.2008.27.1.63
Faboyede, S. (2022). Strategic quality assurance in the auditing profession. Journal of Accounting, Auditing & Finance Research, 9(1), 45-59. https://doi.org/10.15640/jaafr.v9n1a5
Gomber, P., Koch, J.-A., & Siering, M. (2021). Digital finance and FinTech: Current research and future research directions. Journal of Business Economics, 91(5), 555–603. https://doi.org/10.1007/s11573-020-00986-0
Gramling, A., Knechel, W. R., Krishnan, J., & Schwieger, B. (2014). The potential of blockchain technology for audit practice. Current Issues in Auditing, 8(2), 1–10. https://doi.org/10.2308/ciia-52611
Griffith, E. E. (2021). Understanding blockchain technology and its implications for auditing: An introduction. Current Issues in Auditing, 15(2), A1–A16. https://doi.org/10.2308/ciia-52611
Huang, C. S., Lin, K. J., Wang, K. H., & Wei, K. Y. (2020). Data analytics in auditing: Opportunities and challenges. Journal of Information Systems, 34(3), 139–155. https://doi.org/10.2308/isys-52540
Hutchins, M., & Swanson, Z. L. (2021). Integrating blockchain technology into accounting and auditing curricula. Journal of Accounting Education, 57, 100747. https://doi.org/10.1016/j.jaccedu.2021.100747
IFAC. (2021). Enhancing audit quality and relevance for the capital markets. International Federation of Accountants. https://doi.org/10.22617/TALKS.01202021
IIA. (2021). International Standards for the Professional Practice of Internal Auditing. Institute of Internal Auditors.
Jiang, J., & Zhang, W. (2019). Data-driven auditing: Leveraging big data for audit planning and execution. Auditing: A Journal of Practice & Theory, 39(4), 169–188. https://doi.org/10.2308/ajpt-52605
Johnson, J. (2003). The need for changes in audit education. Journal of Accounting Education, 21(1), 1-16. https://doi.org/10.1016/S0748-5751(02)00058-3
Kapula, J. M. (2015). Challenges faced by internal auditors in the corporate sector. International Journal of Auditing, 19(2), 87-102. https://doi.org/10.1111/ijau.12042
Knechel, W. R., & Salterio, S. E. (2016). Auditing: Assurance and risk (4th ed.). Routledge.
Lobo, G. J., Rajan, R., & Selvam, M. (2020). A systematic review of artificial intelligence in auditing: Where are we and where should we be going? Journal of Information Systems, 34(1), 1–29. https://doi.org/10.2308/isys-52454
PCAOB. (2021). 2021 inspection observations. Public Company Accounting Oversight Board. https://doi.org/10.1177/00018392211057607
Peursem, K. A. (2005). Pressures on auditors to compromise independence. The European Accounting Review, 14(2), 339-368. https://doi.org/10.1080/09638180500042928
Pflugrath, G., Martinov-Bennie, N., & Chen, L. (2007). The impact of the Sarbanes-Oxley Act on the corporate governance role of internal audit: Evidence from Australia. Accounting & Finance, 47(4), 717–744. https://doi.org/10.1111/j.1467-629X.2007.00236.x
Power, M. (2016). The audit society: Rituals of verification. Oxford University Press.
Power, M. (2020). Auditing and the production of legitimacy. Journal of Management Studies, 57(7), 1325–1350. https://doi.org/10.1111/joms.12575
Spathis, C., & Doumpos, M. (2014). Data envelopment analysis, clustering, and non-parametric classification techniques for risk evaluation: An integrated approach. Omega, 44, 53–67. https://doi.org/10.1016/j.omega.2013.11.001
Vasarhelyi, M. A., & Halper, F. (2014). Continuous monitoring: A new paradigm for audit? Auditing: A Journal of Practice & Theory, 33(1), 231–260. https://doi.org/10.2308/ajpt-50611
Wen, Y., Wu, S., Chen, X., & Lu, Y. (2020). The effects of Big Data analytics and artificial intelligence on audit procedures: An experimental investigation. Auditing: A Journal of Practice & Theory, 39(4), 169–188. https://doi.org/10.2308/ajpt-52605