Big Data Integration in Auditing: Technological, Institutional, and Ethical Perspectives

DOI: https://doi.org/10.26618/3nxvcp77

Penulis

  • Dandi Aprila Universitas Pembangunan Nasional Veteran Jakarta
  • Anda Dwiharyadi Universitas Pembangunan Nasional Veteran Jakarta

Big Data Auditing; Epistemic Governance; Institutional Theory; Audit Digitalization; Ethical Accountability

Abstrak

The increasing adoption of Big Data technologies has significantly reshaped auditing practices; however, existing scholarship on Big Data auditing remains fragmented and lacks an integrated conceptual perspective. This study aims to systematically examine how Big Data influences audit practices and audit quality across technological, institutional, and epistemic dimensions. Using a Systematic Literature Review (SLR) based on the PRISMA protocol, this study synthesizes evidence from 30 peer-reviewed journal articles indexed in Scopus and Web of Science published between 2015 and 2025. The analysis identifies three dominant research clusters: (1) technological capability, reflecting the development of analytics-driven, continuous, and predictive auditing tools; (2) institutional readiness, highlighting regulatory gaps, organizational resistance, and skill asymmetry; and (3) epistemic transformation, concerning changes in professional judgment, algorithmic transparency, and accountability structures. The findings reveal a progressive datafication of auditing, characterized by a shift from traditional ex-post verification toward real-time and predictive assurance. Although Big Data improves audit efficiency, analytical scope, and risk detection capability, its implementation remains constrained by governance uncertainty and uneven organizational capabilities. To synthesize these insights, this study proposes the Big Data Auditing Framework (BDAF), which conceptualizes audit transformation as the dynamic interaction between Technological Infrastructure, Institutional Adaptation, and Epistemic Governance, moderated by Ethical and Regulatory Oversight. This framework contributes to the literature by offering an integrative perspective on how technological and institutional factors jointly shape the evolution of data-driven auditing and provides practical implications for regulators, educators, and audit firms in strengthening technological capacity, institutional preparedness, and ethical governance in digital audit environments.

Referensi

Ali, A., & Noor, M. (2024). Auditors in the digital age: A systematic review. Journal of Emerging Technologies in Accounting, 21(1), 1–25. https://doi.org/10.2308/jeta-2024-010

Appelbaum, D., Kogan, A., Vasarhelyi, M. A., & Yan, Z. (2017). Impact of business analytics and enterprise systems on managerial accounting. International Journal of Accounting Information Systems, 25, 29–44. https://doi.org/10.1016/j.accinf.2017.03.003

Ballantine, J., Boyce, G., & Stoner, G. (2024). Critical Perspectives on Accounting A critical review of AI in accounting education : Threat and opportunity. Critical Perspectives on Accounting, 99(January), 102711. https://doi.org/10.1016/j.cpa.2024.102711

Biglari, V., & Pourabedin, Z. (2022). Application of Data Analysis and Big Data in Auditing. In T. Chaiechi & J. Wood (Eds.), Community Empowerment, Sustainable Cities, and Transformative Economies (pp. 111–128). Springer Nature Singapore. https://doi.org/10.1007/978-981-16-5260-8_8

Brown-Liburd, H., & Vasarhelyi, M. A. (2023). The role of artificial intelligence in enhancing professional skepticism. Journal of Emerging Technologies in Accounting, 20(2), 1–16. https://doi.org/10.2308/jeta-2023-012

Burneo-arteaga, P., Lira, Y., Murzi, H., Balula, A., Costa, A. P., & Parsazadeh, N. (2025). Capability-based training framework for generative AI in higher education. Frontiers in Education, June. https://doi.org/10.3389/feduc.2025.1594199

Creswell, J. W. (2014). Research design: Qualitative, quantitative, and mixed methods approaches. SAGE Publications.

Dong, Y., Zhang, X., & Lin, S. (2024). Integrating large language models in external audits: Implications for audit quality and accountability. International Journal of Accounting Information Systems, 54(1), 100625. https://doi.org/10.1016/j.accinf.2024.100625

Eulerich, M., Sanatizadeh, A., Vakilzadeh, H., & Wood, D. A. (2024). Is it all hype ? ChatGPT ’ s performance and disruptive potential in the accounting and auditing industries. In Review of Accounting Studies (Vol. 29, Issue 3). Springer US. https://doi.org/10.1007/s11142-024-09833-9

Fábio, A., Dos, G., & Paula, S. (2024). Exploring ChatGPT ’ s capabilities in solving accounting standards problems : the case of IAS. Cogent Education, 11(1), 1–32. https://doi.org/10.1080/2331186X.2024.2412492

Garcia, E., & Lopez, D. (2024). Integrating generative AI into accounting education: A competency development framework. Electronics, 14(3), 152. https://doi.org/10.3390/electronics14030152

Hassan, R., & Al-Qudah, K. (2024). Integration of large language models in accounting: Evidence from emerging economies. Future Business Journal, 10(1), 55–72. https://doi.org/10.1186/s43093-024-00368-8

Isa, H., & Subramanian, U. (2024). The Impact of Big Data in Auditing. Procedia Computer Science, 238(2023), 841–848. https://doi.org/10.1016/j.procs.2024.06.101

Kim, J., & Yoon, S. (2024). Teaching accounting in the era of ChatGPT: Opportunities, ethics, and pedagogical shifts. International Journal of Accounting Education, 32(12), 100511. https://doi.org/10.1016/j.ijedudev.2024.100511

Leitner-hanetseder, S., Perkhofer, L., Frenkenberger, S., & Eisl, C. (2025). ChatGPT as digital accounting assistant : evaluating output performance in financial accounting tasks. October. https://doi.org/10.1108/IJAIM-01-2025-0017

Leoc, D. (2024). Artificial Intelligence in Auditing : A Conceptual Framework for Auditing Practices. Administrative Sciences, 14(238), 1–16. https://doi.org/10.3390/admsci14100238

Leocádio, C., dos Santos, F., & Costa, M. (2025). Continuous auditing and predictive analytics: Evidence from AI-enabled assurance environments. Administrative Sciences, 15(2), 152. https://doi.org/10.3390/admsci15020152

Mistry, R., & Gupta, P. (2025). ChatGPT as a digital accounting assistant: IFRS vs GAAP performance. International Journal of Accounting and Information Management, 33(2), 97–121. https://doi.org/10.1108/IJAIM-01-2025-0017

Mökander, J. (2024). Auditing large language models: A three-layered approach. AI and Ethics, 4(2), 231–247. https://doi.org/10.1007/s43681-023-00289-2

Nguyen, T., & Pham, D. (2024). Exploring ChatGPT’s capabilities in solving accounting standards problems: The case of IAS 37. Journal of Accounting Research and Practice, 19(3), 201–222. https://doi.org/10.1080/07485751.2024.2040847

Power, M. (2024). Algorithmic auditing and the epistemic inversion of assurance. Accounting, Organizations and Society, 115, 101501. https://doi.org/10.1016/j.aos.2024.101501

Rahman, S., & Abdullah, W. (2024). AI in auditing education: Opportunities and challenges. Electronics, 13(26), 2621. https://doi.org/10.3390/electronics13262621

Salar, M., & Umer, H. (2024). ChatGPT in finance : Applications , challenges , and solutions. Heliyon, 10(2), e24890. https://doi.org/10.1016/j.heliyon.2024.e24890

Santos, J., & Leite, F. (2023). Artificial intelligence in auditing: A conceptual framework. Administrative Sciences, 13(1), 89. https://doi.org/10.3390/admsci13010089

Santos, L., & Pereira, F. (2024). Designing AI agents for auditing: Applying large language models. Journal of Emerging Technologies in Accounting, 21(2), 76–101. https://doi.org/10.2308/jeta-2024-024

Saud, I. M., Sofyani, H., Utami, T. P., Mukhlish, M., & Fathmaningrum, E. S. (2025). Big data analytics-based auditing adoption in public sector : Indonesian evidence. Cogent Business & Management, 12(1), 1–22. https://doi.org/10.1080/23311975.2025.2454320

Sofyani, H., Amalia, R., Abu Hasan, H., & Saleh, Z. (2025). Big data analytics in enhancing public sector auditing: drivers, benefits and the moderating role of auditor certification. Journal of Accounting in Emerging Economies, 16(1), 31–50. https://doi.org/10.1108/JAEE-12-2024-0538

Tang, F., & Karim, K. E. (2018). Big data in auditing: Opportunities and challenges. Accounting Horizons, 32(3), 79–93. https://doi.org/10.2308/acch-52098

Tang, F., & Karim, K. E. (2024). Automation bias in audit decision-making: Evidence from AI-supported environments. Current Issues in Auditing, 18(1), 33–47. https://doi.org/10.2308/ciia-2024-011

Thomas, J., & Harden, A. (2008). Methods for the Thematic Synthesis of Qualitative Research in Systematic Reviews. BMC Medical Research Methodology, 8(1), 45. https://doi.org/10.1186/1471-2288-8-45

Warren, J. D., Moffitt, K. C., & Byrnes, P. (2023). The transformation of audit evidence in the era of artificial intelligence. Accounting Horizons, 37(4), 75–92. https://doi.org/10.2308/acch-2023-020

Wu, C., & Zhang, R. (2023). Big data and epistemic governance in auditing: A conceptual synthesis. AI and Ethics, 3(4), 563–582. https://doi.org/10.1007/s43681-023-00289-2

Yuan, Q., & Huang, L. (2024). Big data auditing and algorithmic assurance: A scoping review. Critical Perspectives on Accounting, 96, 102617. https://doi.org/10.1016/j.cpa.2023.102617

Unduhan

Diterbitkan

2026-03-31

Terbitan

Vol 8 No 1 (2026): March 2026

Bagian

Articles

Lisensi

Hak Cipta (c) 2026 Invoice : Jurnal Ilmu Akuntansi

Creative Commons License

Artikel ini berlisensi Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Authors who publish with Invoice: Jurnal Ilmu Akuntansi agree to the following terms:

  1. Copyright Ownership
    The copyright of all articles published in this journal remains with the author(s). However, the authors grant Invoice: Jurnal Ilmu Akuntansi the right of first publication with the work simultaneously licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). This license allows others to share, copy, redistribute, adapt, and build upon the work for any purpose, even commercially, as long as proper credit is given to the original author(s) and the source.

  2. Licensing and Access
    Invoice: Jurnal Ilmu Akuntansi provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge. All published materials are available freely without subscription or payment and can be accessed, downloaded, and reused by any user provided that appropriate attribution is given.

  3. Permission for Reuse
    For uses not covered by the CC BY 4.0 license, such as commercial reprints, translations, or any form of adaptation without clear attribution, users must obtain written permission from the editorial team. Requests for such permissions can be directed to the editorial office at: [invoice@unismuh.ac.id].

  4. Plagiarism and Originality
    Authors are responsible for the originality of their submissions. All articles are screened for plagiarism using appropriate tools before acceptance. Manuscripts found to contain unoriginal content or infringing materials will be rejected or retracted as per journal policy.