Analyzing the Effect of Financial Process Intelligence on Reducing Operational Risk and Improving the Efficiency of Internal Control Systems
Keywords:
financial intelligence, operational risk, internal control, internal auditing, process automation, data analytics, accounting information systemsAbstract
This study aimed to explain how the intelligence of financial processes contributes to reducing operational risk and improving the efficiency of internal control systems in organizations located in Tehran. This study adopted a qualitative research design using thematic analysis. The participants consisted of 18 financial managers, internal auditors, risk managers, financial IT managers, and accounting information system specialists working in companies and financial institutions in Tehran. Participants were selected through purposive sampling, and recruitment continued until theoretical saturation was achieved. Data were collected exclusively through semi-structured interviews. All interviews were transcribed, organized, coded, and analyzed using NVivo software. The analysis followed an inductive thematic procedure, including familiarization with the data, initial coding, theme development, theme review, theme definition, and final reporting. The findings revealed five major mechanisms through which financial process intelligence reduces operational risk and strengthens internal control efficiency: standardization and automation of financial operations, continuous monitoring and anomaly detection, reinforcement of segregation of duties and traceability, improvement of data quality and system integration, and transformation of internal auditing toward analytical and preventive assurance. Participants emphasized that intelligent financial systems reduce human error, late detection of discrepancies, reliance on manual controls, and unauthorized manipulation of financial data. However, they also noted that weak data governance, insufficient digital competence, and excessive reliance on algorithms may create new operational risks. The study concludes that financial process intelligence can sustainably reduce operational risk and improve internal control efficiency when it is not treated merely as a technical automation project. Instead, it should be implemented as an integrated governance mechanism that combines reliable data, human accountability, continuous monitoring, traceable controls, algorithmic validation, and periodic assurance.
Downloads
References
Alles, M. G. (2015). Drivers of the use and facilitators and obstacles of the evolution of Big Data by the audit profession. Accounting Horizons, 29(2), 439–449. https://doi.org/10.2308/acch-51067
Alles, M. G., Kogan, A., & Vasarhelyi, M. A. (2006). Continuous monitoring of business process controls: A pilot implementation of a continuous auditing system at Siemens. International Journal of Accounting Information Systems, 7(2), 137–161. https://doi.org/10.1016/j.accinf.2005.10.004
Appelbaum, D., Kogan, A., & Vasarhelyi, M. A. (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-51684
Basel Committee on Banking Supervision. (2021). Revisions to the principles for the sound management of operational risk. Bank for International Settlements.
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa
Committee of Sponsoring Organizations of the Treadway Commission. (2013). Internal control—Integrated framework. COSO.
Committee of Sponsoring Organizations of the Treadway Commission. (2017). Enterprise risk management—Integrating with strategy and performance. COSO.
Creswell, J. W., & Poth, C. N. (2018). Qualitative inquiry and research design: Choosing among five approaches (4th ed.). SAGE Publications.
Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108–116.
Dechow, N., & Mouritsen, J. (2005). Enterprise resource planning systems, management control and the quest for integration. Accounting, Organizations and Society, 30(7–8), 691–733. https://doi.org/10.1016/j.aos.2004.11.004
Gepp, A., Linnenluecke, M. K., O’Neill, T. J., & Smith, T. (2018). Big Data techniques in auditing research and practice: Current trends and future opportunities. Journal of Accounting Literature, 40, 102–115. https://doi.org/10.1016/j.acclit.2017.05.003
Institute of Internal Auditors. (2020). The IIA’s three lines model: An update of the three lines of defense. The Institute of Internal Auditors.
International Organization for Standardization. (2023). ISO/IEC 42001:2023: Information technology—Artificial intelligence—Management system. ISO.
Kokina, J., & Davenport, T. H. (2017). The emergence of artificial intelligence: How automation is changing auditing. Journal of Emerging Technologies in Accounting, 14(1), 115–122. https://doi.org/10.2308/jeta-51730
Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic inquiry. SAGE Publications.
Moffitt, K. C., Rozario, A. M., & Vasarhelyi, M. A. (2018). Robotic process automation for auditing. Journal of Emerging Technologies in Accounting, 15(1), 1–10. https://doi.org/10.2308/jeta-10589
National Institute of Standards and Technology. (2023). Artificial intelligence risk management framework (AI RMF 1.0). U.S. Department of Commerce. https://doi.org/10.6028/NIST.AI.100-1
Richins, G., Stapleton, A., Stratopoulos, T. C., & Wong, C. (2017). Big Data analytics: Opportunity or threat for the accounting profession? Journal of Information Systems, 31(3), 63–79. https://doi.org/10.2308/isys-51805
Van der Aalst, W. M. P. (2016). Process mining: Data science in action (2nd ed.). Springer. https://doi.org/10.1007/978-3-662-49851-4
