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Abstract

Purpose: This study aims to analyze how Big Data and Big Data Analytics (BDA) are used to strengthen analytical audit procedures in accordance with ISA 520, while identifying the benefits, implementation challenges, and research development directions.


Research Method: The study used a Systematic Literature Review (SLR) approach with the PRISMA 2020 guidelines. The identification, selection, and thematic synthesis processes were carried out for 10 articles on Big Data, analytical procedures, and audits in accordance with professional standards.


Results and Discussion: Findings show that BDA strengthens analytical procedures at all stages of the audit, namely planning, substantive testing, and closing. Big data analytics enables comprehensive population analysis, more accurate expectations, and more precise anomaly detection than traditional techniques. Big Data can improve audit quality by enabling more accurate risk identification and greater process efficiency, potentially reducing audit delays. However, implementation faces obstacles related to auditor competence, infrastructure readiness, varying data quality, and privacy and information security risks.


Implications: This study clarifies the mechanism for integrating Big Data into analytical procedures in accordance with ISA 520. It supports the need for technical guidelines, analytical models, and the strengthening of auditors' digital audit capabilities.

Keywords

big data big data analytics analytical procedures ISA 520 audit quality audit analytics

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How to Cite
Pasulu, M. S., Nisa, K. T., Arifuddin, A., & Syamsuddin, S. (2026). The Use of Big Data in Analytical Audit Procedures: A Literature Review. Advances in Management & Financial Reporting, 4(1), 75–93. https://doi.org/10.60079/amfr.v4i1.677
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References

  1. Abdelwahed, A. S., Abu-Musa, A. A. E. S., Badawy, H. A. E. S., & Moubarak, H. (2024). Investigating the impact of adopting big data and data analytics on enhancing audit quality. Journal of Financial Reporting and Accounting, ahead-of-p(ahead-of-print). https://doi.org/10.1108/JFRA-12-2023-0724
  2. Abdelwahed, A. S., Abu-Musa, A. A., Moubarak, H., & Badawy, H. A. (2024). The use of big data and analytics in external auditing: does audit firm size matter? Evidence from a developing country. South African Journal of Accounting Research, 38(2), 113–145. https://doi.org/10.1080/10291954.2023.2279751
  3. Ahmad, F. (2019). A systematic review of the role of Big Data Analytics in reducing the influence of cognitive errors on the audit judgement: Una revisión sistemática del papel del" Big Data Analytics" en la reducción de la influencia de los errores cognitivos en el juicio. Revista de Contabilidad-Spanish Accounting Review, 22(2), 187–202. https://doi.org/10.6018/RCSAR.382251
  4. Al-Ateeq, B., Sawan, N., Al-Hajaya, K., Altarawneh, M., & Al-Makhadmeh, A. (2022). Big data analytics in auditing and the consequences for audit quality: A study using the technology acceptance model (TAM). Corporate Governance and Organizational Behavior Review, 6(1), 64–78. https://doi.org/10.22495/cgobrv6i1p5
  5. Al-Hajaia, M. E. (2022). The Extent of Analytical Procedures Implementation (International Standard on Auditing No. 520) in Limited Liability Companies. International Journal of Academic Research in Business and Social Sciences, 12(9), 1–14. https://doi.org/10.6007/IJARBSS/v12-i9/14929
  6. Al Qtaish, H., Makhlouf, M. H., & Joudeh, A. H. (2022). The Effect of Auditors’ Use of Analytical Procedures in the Light of ISA 520 on Audit Quality: Evidence from Jordan. Studies of Applied Economics, 40(1). https://doi.org/10.25115/eea.v40i1.6637
  7. Almeida-Blacio, J. H. (2025). Aplicación de Big Data y Técnicas Avanzadas en el Uso de Tecnología dentro de la Auditoría. Horizon Nexus Journal, 3(1), 55–68. https://doi.org/10.70881/hnj/v3/n1/49
  8. Childers, T. L., Carr, C. L., Peck, J., & Carson, S. (2001). Hedonic and utilitarian motivations for online retail shopping behavior. Journal of Retailing, 77(4), 511–535. https://doi.org/https://doi.org/10.1016/S0022-4359(01)00056-2
  9. Chiu, C., Wang, E. T. G., Fang, Y., & Huang, H. (2014). Understanding customers’ repeat purchase intentions in B2C e‐commerce: the roles of utilitarian value, hedonic value and perceived risk. Information Systems Journal, 24(1), 85–114. https://doi.org/10.1111/j.1365-2575.2012.00407.x
  10. Chu, M. K., & Yong, K. O. (2021). Big data analytics for business intelligence in accounting and audit. Open Journal of Social Sciences, 9(9), 42–52. https://doi.org/10.4236/jss.2021.99004
  11. Dagilienė, L., & Klovienė, L. (2019). Motivation to use big data and big data analytics in external auditing. Managerial Auditing Journal, 34(7), 750–782. https://doi.org/10.1108/MAJ-01-2018-1773
  12. Davis, F. D. (1989). Technology acceptance model: TAM. Al-Suqri, MN, Al-Aufi, AS: Information Seeking Behavior and Technology Adoption, 205, 219.
  13. De Santis, F., & D’Onza, G. (2021). Big data and data analytics in auditing: in search of legitimacy. Meditari Accountancy Research, 29(5), 1088–1112. https://doi.org/10.1108/MEDAR-03-2020-0838
  14. Ditkaew, K., & Suttipun, M. (2023). The impact of audit data analytics on audit quality and audit review continuity in Thailand. Asian Journal of Accounting Research, 8(3), 269–278. https://doi.org/10.1108/AJAR-04-2022-0114
  15. Flavián, C., Ibáñez-Sánchez, S., & Orús, C. (2019). The impact of virtual, augmented and mixed reality technologies on the customer experience. Journal of Business Research, 100, 547–560. https://doi.org/https://doi.org/10.1016/j.jbusres.2018.10.050
  16. Foehr, T. L., Reichelt, V., Marten, K.-U., & Eulerich, M. (2025). A framework for the structured implementation of process mining for audit tasks. International Journal of Accounting Information Systems, 56, 100727. https://doi.org/10.1016/j.accinf.2025.100727
  17. Föhr, T. L., Reichelt, V., Marten, K.-U., & Eulerich, M. (2025). A Framework for the Structured Implementation of Process Mining for Audit Tasks. International Journal of Accounting Information Systems, 56, 100727. https://doi.org/https://doi.org/10.1016/j.accinf.2025.100727
  18. Haryanto, A. B., & Setiawan, E. (2024). Impact of Big Data Analytics on Audit Quality With Audit Delay as Mediator. International Journal of Environmental, Sustainability, and Social Science, 5(4), 1007–1014. https://doi.org/10.38142/ijesss.v5i4.1120
  19. Herath, S. K., & Hamm, A. (2023). How big data analytics is used in forensic accounting and auditing. The Business and Management Review, 14(1), 125–132. https://doi.org/10.24052/bmr/v14nu01/art-12
  20. Hezam, Y. A. A., Anthonysamy, L., & Suppiah, S. D. K. (2023). Big data analytics and auditing: A review and synthesis of literature. Emerging Science Journal, 7(2), 629–642. https://doi.org/10.28991/ESJ-2023-07-02-023
  21. Javornik, A. (2016). Augmented reality: Research agenda for studying the impact of its media characteristics on consumer behaviour. Journal of Retailing and Consumer Services, 30, 252–261. https://doi.org/https://doi.org/10.1016/j.jretconser.2016.02.004
  22. Kaya, I., Akbulut, D. H., & Ozoner, K. (2018). Big data analytics in internal audit. PressAcademia Procedia, 7(1), 260–262. https://doi.org/10.17261/Pressacademia.2018.893
  23. Kend, M., & Nguyen, L. A. (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
  24. 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, 100511. https://doi.org/https://doi.org/10.1016/j.accinf.2021.100511
  25. Kritzinger, J., & Barac, K. (2017). The application of analytical procedures in the audit process: A South African perspective. Southern African Business Review, 21(1), 243–273. https://hdl.handle.net/10520/EJC-8e1081dc9
  26. Kuusinen, H., & Miettinen, V. (2023). The Role of Data Analytics in Audit Risk Assessment. https://urn.fi/URN:NBN:fi-fe2023050741634%0A
  27. Matrood, A. K., & Khilkhal, N. S. (2019). The Impact of Applying Analytical Procedures by External Auditor in Accordance with ISA 520 on Audit Performance Improvement: An Exploratory Study in the Iraqi Audit Firms and Companies. Academy of Accounting and Financial Studies Journal, 23(1), 1–21.
  28. McLean, G., & Wilson, A. (2019). Shopping in the digital world: Examining customer engagement through augmented reality mobile applications. Computers in Human Behavior, 101, 210–224. https://doi.org/https://doi.org/10.1016/j.chb.2019.07.002
  29. Mikalef, P., Sharma, K., Pappas, I. O., & Giannakos, M. (2021). Seeking Information on Social Commerce: An Examination of the Impact of User- and Marketer-generated Content Through an Eye-tracking Study. Information Systems Frontiers, 23(5), 1273–1286. https://doi.org/10.1007/s10796-020-10034-3
  30. Mohammed Ismail, I. H., & Abdul Hamid, F. Z. (2024). A systematic literature review of the role of big data analysis in financial auditing. Management & Accounting Review (MAR), 23(2), 321–350. https://doi.org/10.24191/mar.v23i02-14
  31. Narwal, P., & Rai, S. (2022). Individual differences and moral disengagement in Pay-What-You-Want pricing. Journal of Business Research, 149, 528–547. https://doi.org/https://doi.org/10.1016/j.jbusres.2022.05.029
  32. Nasta, L., Magnanelli, B. S., & Alessi, G. (2024). Beyond the numbers: exploring the multifaceted impact of big data analytics on auditing. International Journal of Auditing Technology, 5(1), 40–58. https://doi.org/10.1504/IJAUDIT.2024.139976
  33. Nguyen, P. T. (2023). The application of analytical procedures in Big Four audit firms in Vietnam. Global Academy of Training and Research (GATR) Enterprise. https://doi.org/10.35609/afr.2023.8.3(3)
  34. 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. (2021). The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Bmj, 372. https://doi.org/10.1136/bmj.n71
  35. Park, C., & Kim, Y. (2003). Identifying key factors affecting consumer purchase behavior in an online shopping context. International Journal of Retail & Distribution Management, 31(1), 16–29. https://doi.org/10.1108/09590550310457818
  36. Poushneh, A., & Vasquez-Parraga, A. Z. (2017). Discernible impact of augmented reality on retail customer’s experience, satisfaction and willingness to buy. Journal of Retailing and Consumer Services, 34, 229–234. https://doi.org/https://doi.org/10.1016/j.jretconser.2016.10.005
  37. Pratama, F. W., & Komariyah, E. F. (2023). Examining the auditors’ acceptance of big data analytics technology platform: Evidence from government auditors in Indonesia. The Indonesian Journal of Accounting Research, 26(2), 273–302. https://doi.org/10.33312/ijar.714
  38. Putra, N. S., Ritchi, H., & Alfian, A. (2023). Hubungan Big Data Analytics terhadap Kualitas Audit: Penerapan pada Instansi Pemerintah. Jurnal Riset Akuntansi Dan Keuangan, 11(1), 57–72. https://doi.org/10.17509/jrak.v11i1.55139
  39. Rozana, A. shafa A., Winarningsih, S., & Yadiati, W. (2025). The Impact of Big Data Analytics on Audit Quality in the Digital Era. JASa (Jurnal Akuntansi, Audit Dan Sistem Informasi Akuntansi), 9(2 SE-Articles), 368–377. https://doi.org/10.36555/jasa.v9i2.2878
  40. Salijeni, G., Samsonova-Taddei, A., & Turley, S. (2021). Understanding How Big Data Technologies Reconfigure the Nature and Organization of Financial Statement Audits: A Sociomaterial Analysis. European Accounting Review, 30(3), 531–555. https://doi.org/10.1080/09638180.2021.1882320
  41. Sayedahmed, N., Anwar, S., & Shukla, V. K. (2022). Big data analytics and internal auditing: a review. 2022 3rd International Conference on Computation, Automation and Knowledge Management (ICCAKM), 1–5. https://doi.org/10.1109/ICCAKM54721.2022.9990045
  42. Schneider, D. (1987). Agency Costs and Transaction Costs: Flops in the Principal-Agent-Theory of Financial Markets BT - Agency Theory, Information, and Incentives (G. Bamberg & K. Spremann (eds.); pp. 481–494). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-75060-1_25
  43. Shalihah, D. K., & Ramadhanti, W. (2025). Kontribusi Big Data Analytics dalam Proses Audit Terhadap Audit Delay dan Kualitas Audit. Jurnal Ekonomi Manajemen Akuntansi, 31(1), 215–226.
  44. Sinosi, S. M., Moerdianto, R., Pontoh, G. T., & Mediaty, M. (2022). Implementasi Big Data analystics dalam praktik audit pada perusahaan: Literature review. Jurnal Ekonomi Dan Bisnis, 11(1), 195–203.
  45. Sofyani, H., Rohman, W. M., Della Oktavia, K., & Efsari, A. N. (2025). Big Data Analytics-Based Audit System Quality And Public Sector Audit Performance: Audit Judgment As Mediator. Jurnal Reviu Akuntansi Dan Keuangan, 15(1). https://doi.org/10.22219/jrak.v15i1.36375
  46. Tang, J., & Karim, K. E. (2019). Financial fraud detection and big data analytics – implications on auditors’ use of fraud brainstorming session. Managerial Auditing Journal, 34(3), 324–337. https://doi.org/10.1108/MAJ-01-2018-1767
  47. Tušek, B., Ježovita, A., & Halar, P. (2021). The importance and differences of analytical procedures’ application for auditing blockchain technology between external and internal auditors in Croatia. Economic Research-Ekonomska Istraživanja, 34(1), 1385–1408. https://doi.org/10.1080/1331677X.2020.1828129
  48. Vitali, S., & Giuliani, M. (2024). Emerging digital technologies and auditing firms: Opportunities and challenges. International Journal of Accounting Information Systems, 53, 100676. https://doi.org/https://doi.org/10.1016/j.accinf.2024.100676
  49. Zhao, L., Fu, B., & Bai, S. (2025). Understanding the Influence of Personalized Recommendation on Purchase Intentions from a Self-Determination Perspective: Contingent upon Product Categories. Journal of Theoretical and Applied Electronic Commerce Research, 20(1), 32. https://doi.org/10.3390/jtaer20010032