Transforming Financial Auditing in the Era of Artificial Intelligence
A Mixed Methods Study to Improve Efficiency and Accuracy
DOI:
https://doi.org/10.70062/globalmanagement.v2i2.199Keywords:
Artificial intelligence, Audit accuracy, Audit efficiency, Financial audit, Mixed methodsAbstract
The rapid development of artificial intelligence (AI) presents new challenges and opportunities in the practice of financial auditing, especially regarding the efficiency and accuracy of audit results, which are still the main problems. This study aims to examine the impact of financial audit transformation through the application of AI using a mixed methods approach. Quantitative data were collected from 100-150 internal and external auditors in medium to large companies in East Java Province who have been using AI for at least one year, and analysed using Structural Equation Modeling (SEM) with AMOS. Qualitative data was obtained through in-depth interviews to explore perceptions and challenges of AI implementation. The results showed that AI significantly improved the efficiency of the audit process and the accuracy of risk and fraud detection, despite barriers such as change resistance and limited auditor competence. This research makes important contributions to the development of modern audit theory and offers strategic recommendations for practitioners and regulators to optimise AI integration in financial auditing. Practical implications include the need for auditor training and strengthening technology infrastructure to support sustainable digital transformation.
References
Almaqtari, F., "Challenges in AI adoption in auditing: Data privacy and ethical concerns," Journal of Accounting Research, vol. 12, no. 3, pp. 145–160, 2024.
Antwi, K., "AI in financial auditing: Opportunities and challenges," Journal of Economic Education and Entrepreneurship Study, vol. 5, no. 4, pp. 591–600, 2024.
Ariyanto, D., "Data privacy and cybersecurity risks in AI-powered auditing," Journal of Information Security, vol. 15, no. 2, pp. 120–135, 2024.
Astuti, N., Kholmi, M., and Bella, S., "Machine learning for fraud detection in financial audits," International Journal of Financial Studies, vol. 9, no. 4, pp. 45–60, 2021.
Bakrie.ac.id, "Sampling techniques in financial research," 2024. [Online]. Available: https://bakrie.ac.id/research/sampling
Bella, S., "Predictive analytics in auditing: Enhancing decision making," Journal of Accounting Automation, vol. 7, no. 1, pp. 33–48, 2023.
Brazilian Journal of Business, "Artificial intelligence in financial auditing: Improving efficiency and fraud detection," Brazilian Journal of Business, vol. 7, no. 1, pp. 1–24, 2025.
Byrne, B. M., Structural equation modelling with AMOS: Basic concepts, applications, and programming, 3rd ed. Routledge, 2016.
Chen, L., et al., "Algorithmic bias in AI auditing systems," Journal of Business Ethics, vol. 178, no. 2, pp. 345–360, 2023.
Creswell, J. W., and Plano Clark, V. L., Designing and conducting mixed methods research, 3rd ed. SAGE Publications, 2018.
Creswell, J. W., Research design: Qualitative, quantitative, and mixed methods approaches, 4th ed. SAGE Publications, 2014.
Damayanti, R., and Bambang, S., "Human judgement and AI integration in audit processes," Journal of Accounting and Finance, vol. 18, no. 2, pp. 200–215, 2024.
Fülöp, M., et al., "Ethical standards for AI use in auditing," International Journal of Auditing, vol. 28, no. 1, pp. 55–70, 2023.
Hair, J. F., Black, W. C., Babin, B. J., and Anderson, R. E., Multivariate data analysis, 8th ed. Cengage Learning, 2019.
Hamzah, A., Noordin, N., and Yanto, T., "Opportunities and challenges in integrating artificial intelligence in financial audits," Journal of Economic Education and Entrepreneurship Study, vol. 5, no. 4, pp. 591–600, 2024.
Hasan, R., "AI adoption and sustainable audit quality: A quantitative study," Journal of Theoretical and Applied Information Technology, vol. 103, no. 2, pp. 550–560, 2022.
Ibrahim, M., and Abdou, H., "The impact of using data analytics in the audit process on audit efficiency," International Journal of Accounting and Finance, vol. 12, no. 3, pp. 150–165, 2022.
Journal of Finance and Banking Science (JIKA), "Artificial intelligence in financial reports: How it affects the process's effectiveness and efficiency," IF, vol. 13, no. 2, pp. 257–272, 2024. [Online]. Available: https://doi.org/10.34010/jika.v13i2.12730
Kholmi, M., "AI applications in financial fraud detection," International Journal of Accounting and Finance, vol. 15, no. 2, pp. 120–135, 2024.
Locus, A., "Mixed methods research in auditing: A comprehensive approach," Accounting Research Journal, vol. 17, no. 1, pp. 45–60, 2024.
Mpofu, T., "Human factors in AI adoption in auditing," Accounting Horizons, vol. 37, no. 1, pp. 99–115, 2023.
Munawarah, S., "Auditor competency development in the digital era," Journal of Accounting Studies, vol. 10, no. 3, pp. 210–225, 2024.
Nalgozhina, O., et al., "The impact of artificial intelligence on auditing processes and outcomes," Dynasty Journal of Economics and Finance, vol. 5, no. 4, pp. 4350–4360, 2023.
Noordin, N., and Rikhardsson, P., "Long-term effects of AI in auditing: A critical review," Accounting Horizons, vol. 37, no. 1, pp. 99–115, 2023.
Noordin, N., et al., "Opportunities and challenges in integrating AI in financial audits," Journal of Economic Education and Entrepreneurship Study, vol. 5, no. 4, pp. 591–600, 2022.
O'Donnell, E., "Regulatory frameworks for AI in auditing," International Journal of Auditing, vol. 28, no. 1, pp. 55–70, 2024.
Patcu, C., et al., "Real-time data analysis in auditing using AI technologies," Journal of Accounting and Finance, vol. 18, no. 2, pp. 200–215, 2024.
Peng, Y., et al., "Automation of routine audit tasks through AI: Efficiency and accuracy gains," Journal of Accounting Automation, vol. 7, no. 1, pp. 33–48, 2023.
Puthukulam, A., et al., "Transparency challenges in AI auditing," Journal of Business Ethics, vol. 178, no. 2, pp. 345–360, 2021.
Şahin, S., and Evdilek, M., "Regional impacts on AI adoption in auditing," International Journal of Auditing, vol. 28, no. 1, pp. 55–70, 2024.
Singh, A., et al., "Enhancing audit quality through artificial intelligence: Empirical evidence," Journal of Financial Reporting, vol. 11, no. 3, pp. 150–165, 2023.
Taher, M., et al., "Auditor perceptions on AI transparency," Accounting Research Journal, vol. 17, no. 1, pp. 45–60, 2024.
Tulakhodjaeva, G., and Khodjaeva, D., "Big data analytics in financial auditing: Challenges and opportunities," Journal of Accounting and Information Systems, vol. 15, no. 2, pp. 89–105, 2021.
Veeraiah, S., et al., "Data quality issues in AI auditing," International Journal of Financial Studies, vol. 9, no. 4, pp. 45–60, 2022.
Wati, R., "Mixed methods in auditing research: Bridging quantitative and qualitative insights," Accounting Research Journal, vol. 17, no. 1, pp. 45–60, 2024.
Zubaidah, S., Yanto, T., and Kusumawardani, D., "Impact of AI on auditor productivity and audit cycle time," Journal of Accounting Studies, vol. 10, no. 3, pp. 210–225, 2022.
