Article Sidebar

Submitted
26 June 2026
Accepted
19 August 2026
Published
20 August 2026
Download
PDF (Bahasa Indonesia)
Statistic
Read Counter : 116 Download : 105

Downloads

Download data is not yet available.

Main Article Content

Abstract

Artificial Intelligence (AI) semakin berperan dalam mendukung audit internal seiring meningkatnya kompleksitas risiko, volume data, dan tuntutan pengambilan keputusan yang cepat. Meskipun penerapan AI dalam bidang auditing berkembang pesat, kajian yang secara khusus mensintesis transformasi audit internal masih terbatas. Penelitian ini menggunakan pendekatan Systematic Literature Review (SLR) berdasarkan pedoman PRISMA 2020 untuk mensintesis bukti ilmiah mengenai transformasi audit internal melalui AI dari 21 artikel terindeks Scopus yang diterbitkan pada periode 2021–2026. Hasil kajian menunjukkan bahwa Machine Learning merupakan teknologi AI yang paling dominan, diikuti oleh Robotic Process Automation, Hybrid Artificial Intelligence, dan Natural Language Processing. Pemanfaatan AI terutama terdapat pada area Risk Assessment, Audit Data Analytics, dan Audit Decision Making. AI terbukti meningkatkan kualitas analisis, akurasi, kualitas audit, efisiensi operasional, serta dukungan pengambilan keputusan. Namun, implementasinya masih menghadapi kendala berupa keterbatasan kompetensi auditor, kualitas data, kepercayaan terhadap AI, dan isu explainability. Temuan ini menegaskan bahwa AI meningkatkan efektivitas dan nilai tambah fungsi audit internal sekaligus membuka peluang penelitian pada konteks negara berkembang, sektor publik, serta penerapan Generative AI dan Large Language Models.


 Kata Kunci: artificial intelligence; audit internal; risk assessment; audit data analytics

Article Details

License

Copyright (c) 2026 KINDAI

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

References

  1. Abbas, K., Santis, F. De, & Kolbjørnsrud, V. (2026). Artificial Intelligence in Accounting: A Comparative Analysis of adoption in Accounting and Non-accounting Firms. Journal of Accounting and Public Policy, 57. https://doi.org/10.1016/j.jaccpubpol.2026.107433
  2. Adam, I., & Fazekas, M. (2021). Are Emerging Technologies Helping Win The Fight Against Corruption? A Review of The State of Evidence. Information Economics and Policy, 57. https://doi.org/10.1016/j.infoecopol.2021.100950
  3. Fotoh, L. E., & Mugwira, T. (2025). Exploring Large Language Models in External Audits: Implications and Ethical Considerations. International Journal of Accounting Information Systems, 56. https://doi.org/10.1016/j.accinf.2025.100748
  4. Gauthier, M. P., & Brender, N. (2021). How Do The Current Auditing Standards Fit The Emergent Use of Blockchain? Managerial Auditing Journal, 36(3), 365–385. https://doi.org/10.1108/MAJ-12-2019-2513
  5. Goto, M. (2023). Anticipatory Innovation of Professional Services: The Case of Auditing and Artificial Intelligence. Research Policy, 52. https://doi.org/10.1016/j.respol.2023.104828
  6. Hajek, P., Novotny, J., & Munk, M. (2026). Financial Statement Fraud Detection Using Topic-driven Financial Sentiment Analysis. Decision Support Systems, 203. https://doi.org/10.1016/j.dss.2026.114615
  7. Han, H., Shiwakoti, R. K., Jarvis, R., Mordi, C., & Botchie, D. (2023). Accounting and Auditing With Blockchain Technology and Artificial Intelligence: A Literature Review. International Journal of Accounting Information Systems, 48. https://doi.org/10.1016/j.accinf.2022.100598
  8. Hou, J. (2025). A Study on Enhancing the Audit Efficiency of Natural Resource Asset Management Using Artificial Intelligence. Information Resources Management Journal, 38(1). https://doi.org/10.4018/IRMJ.387648
  9. Huang, L., Abrahams, A., Sithipolvanichgul, J., Gruss, R., & Ractham, P. (2025). Identifying Accounting Control Issues From Online Employee Reviews. Data Science and Management, 8, 248–256. https://doi.org/10.1016/j.dsm.2025.02.001
  10. Institute of Internal Auditors. (2024a). Global Internal Audit Standards. https://www.theiia.org/
  11. Institute of Internal Auditors. (2024b). The IIA’s Three Lines Model. https://www.theiia.org/
  12. Kim, S., Downen, T., & Kang, H. (2026). Better Sooner Than Later? Effects of Adopting Drone-enabled Inventory Observation on Auditor Liabilities. Managerial Auditing Journal, 41(3), 557–578. https://doi.org/10.1108/MAJ-01-2025-4642
  13. Kokina, J., Blanchette, S., Davenport, T. H., & Pachamanova, D. (2025). Challenges and Opportunities for Artificial Intelligence in Auditing: Evidence From The Field. International Journal of Accounting Information Systems, 56. https://doi.org/10.1016/j.accinf.2025.100734
  14. Krieger, F., Drews, P., & Velte, P. (2021). Explaining The (Non-) Adoption of Advanced Data Analytics in Auditing: A Process Theory. International Journal of Accounting Information Systems, 41. https://doi.org/10.1016/j.accinf.2021.100511
  15. Lazirko, M., Gu, H., & Sharma, G. (2026). Automating Hypertext Assignment in Audit Documents: A Large Language Model-Based Approach. Accounting Open, 2. https://doi.org/10.1016/j.accop.2026.100004
  16. Leitner-Hanetseder, S., Lehner, O. M., Eisl, C., & Forstenlechner, C. (2021). A Profession in Transition: Actors, Tasks and Roles in AI-based Accounting. Journal of Applied Accounting Research, 22(3), 539–556. https://doi.org/10.1108/JAAR-10-2020-0201
  17. Lombardi, D. R., Kim, M., Sipior, J. C., & Vasarhelyi, M. A. (2025). The Increased Role of Advanced Technology and Automation in Audit: A Delphi Study. International Journal of Accounting Information Systems, 56. https://doi.org/10.1016/j.accinf.2025.100733
  18. Murphy, B., Feeney, O., Rosati, P., & Lynn, T. (2024). Exploring Accounting and AI Using Topic Modelling. International Journal of Accounting Information Systems, 55. https://doi.org/10.1016/j.accinf.2024.100709
  19. Organisation for Economic Co-operation and Development. (2025). Recommendation of The Council on Artificial Intelligence. http://legalinstruments.oecd.org
  20. Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., … Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372. https://doi.org/10.1136/bmj.n71
  21. Shrestha, Y. R., Krishna, V., & von Krogh, G. (2021). Augmenting Organizational Decision-making With Deep Learning Algorithms: Principles, Promises, and Challenges. Journal of Business Research, 123, 588–603. https://doi.org/10.1016/j.jbusres.2020.09.068
  22. Wei, L., Yu, H., & Li, B. (2022). Advanced Artificial Intelligence Model for Financial Accounting Transformation Based on Enterprise Unstructured Text Data. Journal of Organizational and End User Computing, 34(8). https://doi.org/10.4018/joeuc.315023
  23. Werner, M., Wiese, M., & Maas, A. (2021). Embedding Process Mining Into Financial Statement Audits. International Journal of Accounting Information Systems, 41. https://doi.org/10.1016/j.accinf.2021.100514
  24. Yang, J., Amrollahi, A., & Marrone, M. (2024). Harnessing The Potential of Artificial Intelligence: Affordances, Constraints, and Strategic Implications for Professional Services. In Journal of Strategic Information Systems (Vol. 33). Elsevier B.V. https://doi.org/10.1016/j.jsis.2024.101864
  25. Yang, J., Blount, Y., & Amrollahi, A. (2024). Artificial Intelligence Adoption in A Professional Service Industry: A Multiple Case Study. Technological Forecasting and Social Change, 201. https://doi.org/10.1016/j.techfore.2024.123251