Abdoli Abatari, Z., Mali, A., Rostami, A., & Aghaei Chadegani, A. (2024). Future of Auditing from the Perspective of Information Technology, Changing Auditor-Client Relationship and Changing the Concept of Auditing. Professional Auditing Research, 4(16), 66-91.
Aisyah, S. (2025). Detecting fraud in accounting: A systematic review of theories, models, and techniques (2000–2025). Golden Ratio of Finance Management, 5(1), 113–122. https://doi.org/10.52970/grfm.v5i1.486
Alles, M. (2015). Drivers of the use and facilitators and obstacles of the continuous auditing of financial statements. Accounting Horizons, 29(2), 439–452. https://doi.org/10.2308/acch-51067
Almalki, F., & Masud, M. (2025). Financial fraud detection using explainable AI and stacking ensemble methods. arXiv:2505.10050.
Alsulami, R. (2023). Review the recent fraud detection systems for accounting: Blockchain and ML perspectives. International Journal of Computer Applications.
Appelbaum, D., Kogan, A., & Vasarhelyi, M. (2017). Big data and analytics in the modern audit engagement: Research needs. Auditing: A Journal of Practice & Theory, 36(4), 1–27. https://doi.org/10.2308/ajpt-51836
Ayinla, B. S., Asuzu, O. F., Ndubuisi, L. N., Ike, C. U., Atadoga, A., & Adeleye, R. A. (2024). Utilizing data analytics for fraud detection in accounting: A review and case studies. International Journal of Science and Research Archive, 11(01), 1348–1363. https://doi.org/10.30574/ijsra.2024.11.1.0221
Bagherian Kasgari, A., Raisi Vanani, A., Amiri, M., & Homayoun, S. (2024). Identifying financial fraud in public joint-stock companies using financial and non-financial criteria with a machine learning approach. Smart Business Management Studies, 13(50), 99-142. [in Persian]
Bharadwaj, A., El Sawy, O. A., Pavlou, P. A., & Venkatraman, N. (2013). Digital business strategy: Toward a next generation of insights. MIS Quarterly, 37(2), 471–482. https://doi.org/10.25300/MISQ/2013/37:2.3
Bhattacharya, I. (2024). Accounting fraud detection using contextual language and textual analysis. Journal of Financial Crime. https://doi.org/10.1016/j.accinf.2024.100682
Bierstaker, J., Brody, R. G., & Pacini, C. (2006). Accountants’ perceptions regarding fraud detection and prevention methods. Managerial Auditing Journal, 21(5), 520–535. https://doi.org/10.1108/02686900610667217
Chen, Y., & coauthors. (2025). Deep learning in financial fraud detection: Innovations and directions. Data Science Review. https://doi.org/10.1016/j.dsm.2025.08.002
Chen, Y., Zhao, C., Xu, Y., & Nie, C. (2025). Year-over-Year developments in financial fraud detection via deep learning: A systematic literature review. arXiv:2502.00201. arXiv
Compagnino, A. A. (2025). An introduction to machine learning methods for fraud detection: A practitioner’s guide. Applied Sciences Review. https://doi.org/10.3390/app152111787
Cressey, D. R. (1953). Other people’s money: A study in the social psychology of embezzlement. Free Press.
Desai, A., et al. (2024). A machine learning framework for anomaly detection in high-value payment systems. Bank for International Settlements Working Papers. https://doi.org/10.1016/j.jfds.2025.100163
Futurity Proceedings Group. (2025). Blockchain for fraud prevention: Transforming accounting controls and audit trails. Futurity Proceedings.
Gkegkas, M., & coauthors. (2025). Using data analytics in financial statement fraud detection: A systematic literature review. Journal of Risk and Financial Management, 18(11), 598. https://doi.org/10.3390/jrfm18110598
Han, H. (2023). Accounting and auditing with blockchain technology: Impacts on transparency and audit evidence. Accounting Literature Review. https://doi.org/10.1016/j.accinf.2022.100598
Hernández-Aros, L. (2024). Financial fraud detection through the application of machine learning: A systematic review. Humanities & Social Sciences Communications, 11, Article 3606. https://doi.org/10.1057/s41599-024-03606-0
Jans, M., Alles, M., & Vasarhelyi, M. (2014). Continuous auditing of online financial transactions. International Journal of Accounting Information Systems, 15(1), 1–27. https://doi.org/10.1016/j.accinf.2013.11.002
Kamrani, H., & Abedini, B. (2022). Developing a Fraud Detection Model for Financial Statements Using Artificial Neural Network and Support Vector Machine Methods in Companies Listed on the Tehran Bahador Stock Exchange. Accounting and Management Auditing Knowledge, 11(41), 285-314. [in Persian]
Khademi, S. (2024). Forensic accounting and fraud detection in the digital age, 9th International Conference on Management, Accounting, Banking and Economics of Iran, Mashhad. [in Persian]
Leocádio, D., & coauthors. (2024). Artificial Intelligence in auditing: A conceptual framework and systematic review. Governance (MDPI), 14(10), 238. https://doi.org/10.3390/admsci14100238
Mahasani, M., Nemati, Z., Najafi Soha, A., & Sarwari, F. (2021). A Review of Fraud Detection Methods in Financial Statements: Data Mining Techniques, Diamond Theory Perspective and Forecasting Methods, Fourth International Conference on Electrical, Computer and Mechanical Engineering, Tehran. [in Persian]
Mijani, H., & coauthors. (2025). Review of previous research on blockchain adoption in accounting and auditing. BMF Open Journal. https://doi.org/10.61838/bmfopen.293
Mökander, J., & Floridi, L. (2023). Auditing of AI: Legal, ethical and technical approaches. AI & Ethics, 3, 123–142. https://doi.org/10.1007/s44206-023-00074-y
Nonaka, I., & Takeuchi, H. (1995).The knowledge-creating company: How Japanese companies create the dynamics of innovation. Oxford University Press. https://doi.org/10.1093/oso/9780195092691.001.0001
Oladejo, M. T., & Jack, L. (2020). Fraud prevention and detection in a blockchain technology environment: Challenges posed to forensic accountants. International Journal of Economics and Accounting, 9(4), 315–335. https://doi.org/10.1504/IJEA.2020.110162
Parasuraman, R., Sheridan, T. B., & Wickens, C. D. (2000). A model for types and levels of human interaction with automation. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 30(3), 286–297. https://doi.org/10.1109/3468.844354
Parasuraman, R., Sheridan, T. B., & Wickens, C. D. (2000).A model for types and levels of human interaction with automation. IEEE Transactions on Systems, Man, and Cybernetics, 30(3), 286–297. https://doi.org/10.1109/3468.844354
Rahadar, P., & Ghasemi Alvari, A. (2023). Using big data analysis to detect patterns of fraud and financial abuse, 7th International Conference on Knowledge and Technology of the Third Millennium of Economics, Management and Accounting of Iran, Tehran. [in Persian]
Ramos, S., et al. (2024). Bibliometric analysis of artificial intelligence trends in auditing and fraud detection. Contemporary Governance Review, 8(2). https://doi.org/10.22495/cgobrv8i2sip8
Rashidi. M. (2023). Investigating the role of audit quality in fraud detection: The perspective of clients and auditors, 9th National Conference on New Findings in Science and Technology with a Focus on Computer, Management and Accounting, Tehran. [in Persian]
Rezaei, N., Dianti, Z., Gholami, R., & Rahnamae Roudpeshti, F. (2024). Investigating the effect of cognitive styles on auditors' ability to detect fraud. Financial and Auditing Research, 2, 201-242. [in Persian]
Shevchuk, R., et al. (2025). Anomaly detection in blockchain: A systematic review of methods and applications. Applied Sciences (MDPI), 15(15), 8330. https://doi.org/10.3390/app15158330
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540
Winarto, W. W. A., & coauthors. (2025). Bibliometric analysis and visualization: Accounting fraud research trends. Asia-Pacific Finance Journal. https://doi.org/10.21532/apfjournal.v10i1.385
Wolfe, D. T., & Hermanson, D. R. (2004). The fraud diamond: Considering the four elements of fraud. The CPA Journal, 74(12), 38–42.
Zare Bahnemiri, M., Maleki, M., Hassankhani, F., & Ramsheh, M. (2023). Providing a framework for identifying and analyzing key drivers affecting the future of auditing in Iran with a focus on blockchain technology. Empirical Accounting Research, 13(3), 27-56. [in Persian]