ADVANCING AUDIT QUALITY THROUGH EMERGING TECHNOLOGIES: EXPLORING OPPORTUNITIES AND OVERCOMING CHALLENGES
Main Article Content
Jesica Ramadanty
Hasni Yusrianti
Yulia Saftiana
This study investigates how emerging technologies, especially Artificial Intelligence (AI) and Big Data, contribute to improving audit quality. By conducting a systematic literature review of Scopus indexed articles published from 2019 to 2024, the research highlights both the opportunities and challenges involved in implementing these technologies within auditing practices. The findings reveal that AI contributes significantly to audit quality by automating repetitive tasks, improving fraud detection, enhancing data analysis, and enabling more informed auditor judgment. Furthermore, the use of AI and digital tools has been shown to increase efficiency and reduce audit costs. Several challenges persist, including ethical concerns, data security risks, technological readiness disparities among audit firms, and a lack of clear regulatory standards. The study synthesizes theoretical and empirical insights using various frameworks such as Agency Theory, Institutional Theory, and the Technology-Organization-Environment framework, offering a multidimensional understanding of AI adoption in auditing. The results underscore the necessity for auditors to adjust to rapidly advancing technological developments while upholding ethical standards and professional skepticism. This study adds to the expanding discussion on the digital transformation of auditing practices by highlighting the balance between technological innovation and audit integrity.
Adawiyah, D. (2022). Perilaku auditor menyikapi munculnya artificial intelligence dalam proses audit. Jurnal Publikasi Ekonomi Dan Akuntansi, 2(1), 52-60. https://doi.org/10.51903/jupea.v2i1.152
Adeoye, I., Akintoye, I., Aguguom, T., & Olagunju, O. (2023). Artificial intelligence and audit quality: implications for practicing accountants. Asian Economic and Financial Review, 13(11), 756-772. https://doi.org/10.55493/5002.v13i11.4861
al-Ma'aitah, R., Al-Hajaya, K., Sawan, N., & Alzeban, A. (2024). The impact of remote auditing on audit quality: the moderating role of technology readiness. Managerial Auditing Journal, 39(6), 624-647. https://doi.org/10.1108/maj-02-2024-4210
Al-Shaer, H., & Zaman, M. (2022). Artificial intelligence in auditing: Challenges and opportunities. Journal of International Accounting, Auditing and Taxation, 48. https://doi.org/10.1016/j.intaccaudtax.2022.100495
Alotaibi, E. and Alnesafi, A. (2023). Assessing the impact of audit software on audit quality: auditors' perceptions. International Journal of Applied Economics Finance and Accounting, 17(1), 97-108. https://doi.org/10.33094/ijaefa.v17i1.1068
Ananda, R. (2024). Assessment audit: how artificial intelligence affected audit quality of sustainability report based on auditors perspective. Information Management and Business Review, 16(3(I)S), 152-158. https://doi.org/10.22610/imbr.v16i3(i)s.4049
Antwi, et al. (2024). Enhancing audit accuracy: the role of ai in detecting financial anomalies and fraud. Finance & Accounting Research Journal, 6(6), 1049-1068. https://doi.org/10.51594/farj.v6i6.1235
Ayling, J., & Chapman, A. (2021). Putting ai ethics to work: are the tools fit for purpose?. Ai and Ethics, 2(3), 405-429. https://doi.org/10.1007/s43681-021-00084-x
Černevičienė, J., & Kabasinskas, A. (2024). Explainable artificial intelligence (XAI) in finance: a systematic literature review. Artificial Intelligence Review, 57(8). https://doi.org/10.1007/s10462-024-10854-8
Dahabiyeh, L., & Mowafi, O. (2023). Challenges of using rpa in auditing: a socio‐technical systems approach. Intelligent Systems in Accounting Finance & Management, 30(2), 76-86. https://doi.org/10.1002/isaf.1537
DeFond, M. L., & Zhang, J. (2014). A Review of Archival Auditing Research. Journal of Accounting and Economics, 58(2-3), 275–326. https://doi.org/10.1016/j.jacceco.2014.09.002
Federicco, T., & Tandiono, R. (2023). An exploratory study of the familiarity and the perceptions of continuous auditing technology in indonesia. E3s Web of Conferences, 388, 03005. https://doi.org/10.1051/e3sconf/202338803005
Fedyk, A., Hodson, J., Khimich, N., & Fedyk, T. (2022). Is artificial intelligence improving the audit process?. Review of Accounting Studies, 27(3), 938-985. https://doi.org/10.1007/s11142-022-09697-x
Francis, J. (2011). A framework for understanding and researching audit quality. Auditing: A Journal of Practice & Theory, 30(2), 125–152. https://doi.org/10.2308/ajpt-50006
García-Vera, Y., Maldonado, F., & Torres-Gallegos, V. (2023). Automatización de procesos contables mediante inteligencia artificial: oportunidades y desafíos para pequeños empresarios ecuatorianos. Revista Transdiciplinaria De Estudios Sociales Y Tecnológicos, 3(3), 68-74. https://doi.org/10.58594/rtest.v3i3.93
Gepp, A., Linnenluecke, M., O’Neill, T., & Smith, T. (2018). Big data techniques in auditing research and practice: current trends and future opportunities. Journal of Accounting Literature, 40(1), 102-115. https://doi.org/10.1016/j.acclit.2017.05.003
Ikhsan, W., Ednoer, E., Kridantika, W., & Firmansyah, A. (2022). Fraud detection automation through data analytics and artificial intelligence. Riset: Jurnal Aplikasi Ekonomi Akuntansi dan Bisnis, 4(2), 103-119. https://doi.org/10.37641/riset.v4i2.166
International Auditing and Assurance Standards Board (IAASB). (2014). A Framework for Audit Quality: Key Elements that Create an Environment for Audit Quality. International Federation of Accountants. Retrieved from https://www.ifac.org
Irman, M., Suhendra, E., & Diana, H. (2021). Work experience, professionalism, independence and the application of information technology on auditor performance in order to increasing audit quality at the financial audit agency of the republic of indonesia representative of the riau province. Journal of Applied Business and Technology, 2(3), 206-222. https://doi.org/10.35145/jabt.v2i3.78
Kawisana, P., & Jayanti, L. (2022). The effect of audit complexity of budget pressure time and auditor experience on audit quality with an understanding of information systems as a moderate variable. Journal of Tourism Economics and Policy, 2(2), 93-97. https://doi.org/10.38142/jtep.v2i2.353
Kend, M., & Nguyen, L. (2020). Big data analytics and other emerging technologies: the impact on the australian audit and assurance profession. Australian Accounting Review, 30(4), 269-282. https://doi.org/10.1111/auar.12305
Khan, F., Jan, S. U., & Zia-ul-haq, H. M. (2024). Artificial intelligence adoption, audit quality and integrated financial reporting in GCC markets. Asian Review of Accounting. https://doi.org/10.1108/ARA-03-2024-0085
Knechel, W. R., Krishnan, G. V., Pevzner, M., Shefchik, L. B., & Velury, U. K. (2020). Audit quality: Insights from the academic literature. Auditing: A Journal of Practice & Theory, 39(1), 1–38. https://doi.org/10.2308/ajpt-15-009
Kusuma, B. (2024). The impact of the covid-19 pandemic on auditing practices: a qualitative analysis. Golden Ratio of Auditing Research, 4(1), 24-32. https://doi.org/10.52970/grar.v4i1.385
Kokina, J., Pachamanova, D., & Corbett, A. (2021). The role of data analytics and AI in modern auditing. Journal of Emerging Technologies in Accounting, 18(1), 27–50. https://doi.org/10.2308/JETA-2020-003
Lugli, E., & Bertacchini, F. (2022). Audit quality and digitalization: some insights from theitalian context. Meditari Accountancy Research, 31(4), 841-860. https://doi.org/10.1108/medar-08-2021-1399
Luthfiani, A. (2024). The artificial intelligence revolution in accounting and auditing: opportunities, challenges, and future research directions. Journal of Applied Business Taxation and Economics Research, 3(5), 516-530. https://doi.org/10.54408/jabter.v3i5.290
Mihai, M. (2024). Toe framework elements used on artificial intelligence implementation in the accounting and audit sector. International Journal of Research in Business and Social Science, 13(4), 335-349. https://doi.org/10.20525/ijrbs.v13i4.3374
Noordin, N., Hussainey, K., & Hayek, A. (2022). The use of artificial intelligence and audit quality: an analysis from the perspectives of external auditors in the uae. Journal of Risk and Financial Management, 15(8), 339. https://doi.org/10.3390/jrfm15080339
Odeyemi, O., Awonuga, K., Mhlongo, N., Ndubuisi, N., Olatoye, F., & Daraojimba, A. (2023). The role of ai in transforming auditing practices: a global perspective review. World Journal of Advanced Research and Reviews, 21(2), 359-370. https://doi.org/10.30574/wjarr.2024.21.2.0460
Onwubuariri, E., Adelakun, B., Olaiya, O., & Ziorklui, J. (2024). Ai-driven risk assessment: revolutionizing audit planning and execution. Finance & Accounting Research Journal, 6(6), 1069-1090. https://doi.org/10.51594/farj.v6i6.1236
Otia, J., & Bracci, E. (2022). Digital transformation and the public sector auditing: the sai's perspective. Financial Accountability and Management, 38(2), 252-280. https://doi.org/10.1111/faam.12317
Rahman, et al. (2024). Does the adoption of artificial intelligence by audit firms and their clients affect audit quality and efficiency? evidence from china. Managerial Auditing Journal, 39(6), 668-699. https://doi.org/10.1108/maj-03-2023-3846
Ríkharðsson, P., Þórisson, K., Bergthorsson, G., & Batt, C. (2022). Artificial intelligence and auditing in small‐ and medium‐sized firms: expectations and applications. Ai Magazine, 43(3), 323-336. https://doi.org/10.1002/aaai.12066
Russell, S. J., & Norvig, P. (2021). Artificial Intelligence: A Modern Approach (4th ed.). Pearson.
Schreuder, A., & Smuts, H. (2023). Perspective chapter: audit digitalization – key impacts on the audit profession. https://doi.org/10.5772/intechopen.109042
Seethamraju, R., & Hecimovic, A. (2022). Adoption of artificial intelligence in auditing: an exploratory study. Australian Journal of Management, 48(4), 780-800. https://doi.org/10.1177/03128962221108440
Sun, T. & Vasarhelyi, M. (2018). Embracing textual data analytics in auditing with deep learning. IJDAR, 49-67. https://doi.org/10.4192/1577-8517-v18_3
