نوع مقاله : مقاله پژوهشی
عنوان مقاله English
نویسندگان English
Abstract
This research was conducted with the aim of identifying the factors affecting fraud in banks' financial reporting and presenting a conceptual model to improve the supervisory system and enhance financial transparency. The research population consisted of experts in the fields of banking, auditing, financial supervision, and academia, and the sample was selected through purposive and snowball sampling. Qualitative data were collected through 24 semi-structured interviews and analyzed using the Strauss and Corbin grounded theory approach. In the open coding stage, 73 initial codes were extracted, which were then organized in the axial and selective coding phases, utilizing MAXQDA 2020 software, into 6 core categories and five paradigmatic components: causal conditions, contextual conditions, intervening conditions, strategies, and consequences. The findings revealed that the core phenomenon of "fraud detection in banks' financial reporting" is influenced by a set of intra-bank, supervisory and legal, technological, human, environmental, and international factors. The results indicate that weak corporate governance, performance pressures, the complexity of banking activities, opportunities arising from supervisory gaps, and a weak organizational culture are the most significant factors creating a breeding ground for fraud. Furthermore, analytical strategies such as controlling drivers, monitoring rationalization behaviors, data-driven analysis, and whistleblowing, along with technical strategies including fraud risk-based auditing and qualitative content analysis of reports, were identified as the most effective approaches for detecting and mitigating fraud. The consequences of implementing these strategies include increased supervisory efficiency, improved internal controls, enhanced audit quality, reduced future costs, and the strengthening of the banking system's health. The final research model suggests a multidimensional structure that illustrates the interaction between individual, organizational, technological, and environmental factors, and can serve as a basis for improving regulations, designing warning systems, and developing fraud risk assessment frameworks in banks.
کلیدواژهها English
12. Alfarago, D., Syukur, M., & Mabrur, A. (2023). The likelihood of fraud from the Fraud Hexagon perspective: Evidence from Indonesia. ABAC Journal, 43(1), 34–51.
13. Association of Certified Fraud Examiners (ACFE). (2016). Report to the Nations on Occupational Fraud and Abuse. Austin, TX: ACFE.
14. Bishop, T. J. F., & Hydoski, F. E. (2009). Corporate Resiliency: Managing the Growing Risk of Fraud and Corruption. Hoboken, NJ: John Wiley & Sons.
15. Javier-Moreno Arboleda, F., Guzman-Luna, A., & Torres, D. (2018). Fraud detection–oriented operators in a data warehouse based on forensic accounting techniques. Computer Fraud & Security, 10, 13–19.
16. Li, J. (2025). Corporate governance, fraud learning cycles, and financial fraud detection: Evidence from Chinese listed firms. Research in International Business and Finance, 76, 102832.
17. Lalit, W., & Virender, P. (2012). Forensic accounting and fraud examination in India. International Journal of Applied Engineering Research, 7(11), 1–4.
18. Marais, A., Vermaak, C., & Shewell, P. (2023). Predicting financial statement manipulation in South Africa: A comparison of the Beneish and Dechow models. Cogent Economics & Finance, 11(1), 1–33.
19. Marsellisa, N. (2018). Financial statement fraud: Perspective of the Pentagon Fraud Model in Indonesia. Academy of Accounting and Financial Studies Journal, 22(3), 1–9.
20. Nahri Aghdam Qaleh Jouqi, J., Rezaei, N., Aghdam Mazrae, Y., & Abdi, R. (2024). Comparing the performance of machine learning techniques in detecting financial frauds. Advances in Mathematical Finance and Applications, 9(3), 1006–1023. https://doi.org/10.71716/amfa.2024.22101813
21. Nurma Prastika, A., & Sasongko, N. (2023). Analysis of fraudulent financial reporting with Fraud Hexagon Theory in financial sector companies listed on the Indonesia Stock Exchange (2017–2021). International Journal of Business Management and Technology, 7(1), 1–12.
22. Omidi, M., Min, Q., Moradinaftchali, V., & Piri, M. (2019). The efficacy of predictive methods in financial statement fraud. Discrete Dynamics in Nature and Society, 2019, Article ID 1289360.
23. Ratna, S., & Sri, F. (2020). Pengaruh Fraud Pentagon terhadap kecurangan laporan keuangan. Prosiding Akuntansi, 364–369.
24. Rezaee, Z., & Crumbley, L. (2007). The role of forensic auditing techniques in restoring public trust and investor confidence in financial information. The Forensic Examiner, 16(1), 44–63.
25. Sallal, F., Bagherpour Velashani, M. A., & Saei, M. J. (2021). Fraudulent financial reporting motivations in emerging markets. Journal of Financial Crime, 28(3), 892–905.
26. Shemshad, A., & Karim, R. G. (2023). The effect of managerial ability on the timeliness of financial reporting: The role of audit firm and company size. Journal of Operational and Strategic Analytics, 1(1), 34–41.
27. Tiffani, L., & Marfuah, M. (2015). Detection of financial statement fraud using fraud triangle analysis in manufacturing firms listed on the Indonesia Stock Exchange. Jurnal Akuntansi dan Auditing Indonesia, 112–125.
28. Umar, H. (2020). Detecting Corruption: HU Model. Jakarta: Penerbit Universitas Trisakti.
29. Umar, H., Haryono, & Purba, R. (2020). HU Model: Incorporation of Fraud Star in detection of corruption. International Journal of Economics and Management Studies, 13(6), 234–265.
30. Vousinas, G. L. (2019). Advancing theory of fraud: The SCORE model. Journal of Financial Crime, 26(1), 372–381.
31. Wells, J. T. (2017). Corporate Fraud Handbook: Prevention and Detection (6th ed.). John Wiley & Sons.