A Structural Analytical Framework for Technology-Driven Fraud Detection and Audit Outcomes

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DOI: https://doi.org/10.28924/2291-8639-24-2026-130
Published: Apr 30, 2026
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Lai Thi Thu Thuy, A Structural Analytical Framework for Technology-Driven Fraud Detection and Audit Outcomes, Int. J. Anal. Appl., 24 (2026), 130.

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Lai Thi Thu Thuy

Abstract

This paper develops a structural analytical model to examine the relationships between technology-related capabilities and audit outcomes through an intermediate analytical mechanism. Within a quantitative structural analysis framework, the study investigates the effects of artificial intelligence adoption, data analytics capability, and auditor IT competence on fraud detection effectiveness, and examines the mediating role of fraud detection effectiveness in determining audit quality. The empirical model is estimated using survey data from 298 external auditors in Vietnam and analysed via Partial Least Squares–based structural equation modelling. The results show that artificial intelligence adoption, data analytics capability, and auditor IT competence have positive and statistically significant structural effects on fraud detection effectiveness, with artificial intelligence adoption exhibiting the largest standardized coefficient. In addition, fraud detection effectiveness exerts a strong positive effect on audit quality and fully mediates the relationships between technology-related capabilities and audit outcomes. The proposed structural framework provides an applied analytical interpretation of how technology-driven mechanisms influence audit performance and offers implications for the design of technology-enabled audit systems in emerging markets.

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References

  1. A.A. Arens, R.J. Elder, M.S. Beasley, C.E. Hogan, Auditing and Assurance Services: An Integrated Approach, Pearson, 2017.
  2. W.R. Knechel, Audit Quality and Regulation, Int. J. Audit. 20 (2016), 215-223. https://doi.org/10.1111/ijau.12077.
  3. C.L. Becker, M.L. Defond, J. Jiambalvo, et al. The Effect of Audit Quality on Earnings Management, Contemp. Account. Res. 15 (1998), 1-24. https://doi.org/10.1111/j.1911-3846.1998.tb00547.x.
  4. R.L. Watts, J.L. Zimmerman, Agency Problems, Auditing, and the Theory of the Firm: Some Evidence, J. Law Econ. 26 (1983), 613-633. https://doi.org/10.1086/467051.
  5. N.T.T. Binh, Transforming Auditing in the AI Era: A Comprehensive Review, Information 16 (2025), 400. https://doi.org/10.3390/info16050400.
  6. H. Issa, T. Sun, M.A. Vasarhelyi, Research Ideas for Artificial Intelligence in Auditing: The Formalization of Audit and Workforce Supplementation, J. Emerg. Technol. Account. 13 (2016), 1-20. https://doi.org/10.2308/jeta-10511.
  7. J. Kokina, T.H. Davenport, The Emergence of Artificial Intelligence: How Automation Is Changing Auditing, J. Emerg. Technol. Account. 14 (2017), 115-122. https://doi.org/10.2308/jeta-51730.
  8. D. Appelbaum, A. Kogan, M.A. Vasarhelyi, Big Data and Analytics in the Modern Audit Engagement: Research Needs, Auditing: J. Pract. Theory 36 (2017), 1-27. https://doi.org/10.2308/ajpt-51684.
  9. M. Cao, R. Chychyla, T. Stewart, Big Data Analytics in Financial Statement Audits, Account. Horizons 29 (2015), 423-429. https://doi.org/10.2308/acch-51068.
  10. J. Bierstaker, D. Janvrin, D.J. Lowe, What Factors Influence Auditors' Use of Computer-Assisted Audit Techniques?, Adv. Account. 30 (2014), 67-74. https://doi.org/10.1016/j.adiac.2013.12.005.
  11. D. Janvrin, J. Bierstaker, D.J. Lowe, An Examination of Audit Information Technology Use and Perceived Importance, Account. Horizons 22 (2008), 1-21. https://doi.org/10.2308/acch.2008.22.1.1.
  12. R. Fullerton, C. Durtschi, The Effect of Professional Skepticism on the Fraud Detection Skills of Internal Auditors, SSRN (2004). https://doi.org/10.2139/ssrn.617062.
  13. A.S. Abdelwahed, A.A. Abu-Musa, H.A. Badawy, H. Moubarak, Unleashing the Beast: The Impact of Big Data and Data Analytics on the Auditing Profession—Evidence from a Developing Country, Futur. Bus. J. 11 (2025), 12. https://doi.org/10.1186/s43093-024-00420-7.
  14. B.N. Pham, T.Q. Nguyen, M.D. Tran, T.T.P. Nguyen, Auditor's Perception of Usage and Importance of Audit Technology in Vietnam, Asian Bus. Res. 3 (2018), 14. https://doi.org/10.20849/abr.v3i3.513.
  15. J. Barney, Firm Resources and Sustained Competitive Advantage, J. Manag. 17 (1991), 99-120. https://doi.org/10.1177/014920639101700108.
  16. R.M. Grant, Toward a Knowledge‐based Theory of the Firm, Strat. Manag. J. 17 (1996), 109-122. https://doi.org/10.1002/smj.4250171110.
  17. M.C. Jensen, W.H. Meckling, Theory of the Firm: Managerial Behavior, Agency Costs and Ownership Structure, J. Financ. Econ. 3 (1976), 305-360. https://doi.org/10.1016/0304-405X(76)90026-X.
  18. R. Kassem, Investigating the Black Box of External Audit Practice: The Paradox of Auditors' Failure in Detecting and Reporting Fraud, J. Account. Lit. 45 (2023), 406-424. https://doi.org/10.1108/JAL-05-2022-0057.
  19. J. Kokina, S. Blanchette, T.H. Davenport, D. Pachamanova, Challenges and Opportunities for Artificial Intelligence in Auditing: Evidence from the Field, Int. J. Account. Inf. Syst. 56 (2025), 100734. https://doi.org/10.1016/j.accinf.2025.100734.
  20. S.G. Sutton, M. Holt, V. Arnold, “The Reports of My Death Are Greatly Exaggerated”—Artificial Intelligence Research in Accounting, Int. J. Account. Inf. Syst. 22 (2016), 60-73. https://doi.org/10.1016/j.accinf.2016.07.005.
  21. K.H. Hu, F.H. Chen, M.F. Hsu, G.H. Tzeng, Governance of Artificial Intelligence Applications in a Business Audit via a Fusion Fuzzy Multiple Rule-Based Decision-Making Model, Financ. Innov. 9 (2023), 117. https://doi.org/10.1186/s40854-022-00436-4.
  22. T. Awosika, R.M. Shukla, B. Pranggono, Transparency and Privacy: The Role of Explainable AI and Federated Learning in Financial Fraud Detection, IEEE Access 12 (2024), 64551-64560. https://doi.org/10.1109/ACCESS.2024.3394528.
  23. K. Ditkaew, M. Suttipun, The Impact of Audit Data Analytics on Audit Quality and Audit Review Continuity in Thailand, Asian J. Account. Res. 8 (2023), 269-278. https://doi.org/10.1108/AJAR-04-2022-0114.
  24. A. Gandomi, M. Haider, Beyond the Hype: Big Data Concepts, Methods, and Analytics, Int. J. Inf. Manag. 35 (2015), 137-144. https://doi.org/10.1016/j.ijinfomgt.2014.10.007.
  25. N. Singh, K. Lai, M. Vejvar, T.C.E. Cheng, Data‐driven Auditing: A Predictive Modeling Approach to Fraud Detection and Classification, J. Corp. Account. Financ. 30 (2019), 64-82. https://doi.org/10.1002/jcaf.22389.
  26. M. Ghasemaghaei, G. Calic, Can Big Data Improve Firm Decision Quality? the Role of Data Quality and Data Diagnosticity, Decis. Support Syst. 120 (2019), 38-49. https://doi.org/10.1016/j.dss.2019.03.008.
  27. M.B. Curtis, E.A. Payne, Modeling Voluntary CAAT Utilization Decisions in Auditing, Manag. Audit. J. 29 (2014), 304-326. https://doi.org/10.1108/MAJ-07-2013-0903.
  28. E.G. Siew, K. Rosli, P.H. Yeow, Organizational and Environmental Influences in the Adoption of Computer-Assisted Audit Tools and Techniques (CAATTs) by Audit Firms in Malaysia, Int. J. Account. Inf. Syst. 36 (2020), 100445. https://doi.org/10.1016/j.accinf.2019.100445.
  29. M. Tümmler, R. Quick, How to Detect Fraud in an Audit: A Systematic Review of Experimental Literature, Manag. Rev. Q. 76 (2025), 287-332. https://doi.org/10.1007/s11301-024-00480-7.
  30. J.F. Hair, J.J. Risher, M. Sarstedt, C.M. Ringle, When to Use and How to Report the Results of Pls-Sem, Eur. Bus. Rev. 31 (2019), 2-24. https://doi.org/10.1108/EBR-11-2018-0203.
  31. R.W. Brislin, Back-Translation for Cross-Cultural Research, J. Cross-Cultural Psychol. 1 (1970), 185-216. https://doi.org/10.1177/135910457000100301.
  32. W.M. Lim, Philosophy of Science and Research Paradigm for Business Research in the Transformative Age of Automation, Digitalization, Hyperconnectivity, Obligations, Globalization and Sustainability, J. Trade Sci. 11 (2023), 3-30. https://doi.org/10.1108/JTS-07-2023-0015.
  33. C. Fornell, D.F. Larcker, Evaluating Structural Equation Models with Unobservable Variables and Measurement Error, J. Mark. Res. 18 (1981), 39-50. https://doi.org/10.1177/002224378101800104.
  34. J. Henseler, C.M. Ringle, M. Sarstedt, A New Criterion for Assessing Discriminant Validity in Variance-Based Structural Equation Modeling, J. Acad. Mark. Sci. 43 (2014), 115-135. https://doi.org/10.1007/s11747-014-0403-8.
  35. S. Brown, J. Davidovic, A. Hasan, The Algorithm Audit: Scoring the Algorithms That Score US, Big Data Soc. 8 (2021), 2053951720983865. https://doi.org/10.1177/2053951720983865.
  36. I.Y. Hafez, A.Y. Hafez, A. Saleh, A.A. Abd El-Mageed, A.A. Abohany, A Systematic Review of AI-Enhanced Techniques in Credit Card Fraud Detection, J. Big Data 12 (2025), 6. https://doi.org/10.1186/s40537-024-01048-8.
  37. C.S. Lee, F.P. Tajudeen, Usage and Impact of Artificial Intelligence on Accounting: 213 Evidence from Malaysian Organisations, Asian J. Bus. Account. 13 (2020), 213-240. https://doi.org/10.22452/ajba.vol13no1.8.
  38. H. Lu, Z. Wu, Revisiting Intelligent Audit from a Data Science Perspective, Neurocomputing 623 (2025), 129431. https://doi.org/10.1016/j.neucom.2025.129431.
  39. M. Umar, S.M. Sitorus, R.L. Surya, E.R. Shauki, V. Diyanti, Pressure, Dysfunctional Behavior, Fraud Detection and Role of Information Technology in the Audit Process, Australas. Account. Bus. Financ. J. 11 (2017), 102-115. https://doi.org/10.14453/aabfj.v11i4.8.
  40. F.B. Reslan, N.J. Al Maalouf, Assessing the Transformative Impact of AI Adoption on Efficiency, Fraud Detection, and Skill Dynamics in Accounting Practices, J. Risk Financ. Manag. 17 (2024), 577. https://doi.org/10.3390/jrfm17120577.