Analysis Effectiveness of the Internal Control System in Detecting Fraud (Study at PT Arminareka Perdana Group)

Authors

  • Richan Nurhasan Mudzakar Institut Bisnis dan Komunikasi Swadaya, Jakarta, Indonesia.
  • Karsam Institut Bisnis dan Komunikasi Swadaya, Jakarta, Indonesia.
  • Atik Budi Paryanti Institut Bisnis dan Komunikasi Swadaya, Jakarta, Indonesia.

DOI:

https://doi.org/10.38035/dijefa.v7i1.6420

Keywords:

Internal Control System, Fraud Detection, Social Control Theory, Organizational Governance

Abstract

This study analyzes the effectiveness of the internal control system in detecting fraud at PT Arminareka Perdana Group. Based on the COSO framework and Social Control Theory, internal control is positioned not only as a governance mechanism but also as a formal social control instrument that shapes ethical behavior and organizational compliance. Using a quantitative approach with PLS-SEM, data were collected from employees involved in financial supervision and operational control. The findings indicate that effective implementation of control environment, risk assessment, control activities, information and communication, and monitoring strengthens fraud detection through structured procedures, segregation of duties, transparent reporting, and continuous evaluation. Internal control contributes to building an accountable organizational culture and supports early identification of irregularities. The novelty of this research lies in integrating COSO-based internal control with Social Control Theory to explain fraud detection from structural and behavioral perspectives, offering strategic insights for strengthening governance and fraud risk management.

References

Akouhar, M., Ouhssini, M., El Fatini, M., Abarda, A., & Agherrabi, E. (2025). Dynamic oversampling-driven Kolmogorov–Arnold networks for credit card fraud detection: An ensemble approach to robust financial security. Egyptian Informatics Journal, 31(June), 100712. https://doi.org/10.1016/j.eij.2025.100712

Cheng, J., Zhang, G., Abdulla, W., & Sun, J. (2024). Advancing fraud detection in New Zealand Mānuka honey: Integrating hyperspectral imaging and GANomaly-based one-class classification. Food Bioscience, 60(April), 104428. https://doi.org/10.1016/j.fbio.2024.104428

Dong, R., Tan, Q., Tan, Y., Song, X., & Wang, T. (2025). A segmented model based internal model control scheme of electromagnetic micro-mirror systems. ISA Transactions, June, 1–14. https://doi.org/10.1016/j.isatra.2025.06.003

Ebaya, A., Qin, X., & Elsayed, M. (2024). Female board members, financial constraints, and internal control quality: New insights following COSO’s 2013 framework. Economics Letters, 234(November 2023), 111446. https://doi.org/10.1016/j.econlet.2023.111446

El-tahlawy, A. S., Alawam, A. S., Rudayn, H. A., Allam, A. A., Mahmoud, R., El-Raheem, H. A., & Alahmad, W. (2025). Advanced analytical and digital approaches for proactive detection of food fraud as an emerging contaminant threat. Talanta Open, 12(June), 100499. https://doi.org/10.1016/j.talo.2025.100499

Farah, N., Islam, M. S., Tadesse, A., & McCumber, W. (2024). Impact of audit committee social capital on the adoption of COSO 2013. Advances in Accounting, 64(June 2022), 100685. https://doi.org/10.1016/j.adiac.2023.100685

Hassan, K., Axelsson, S., Li, Y., & Makki, A. (2025). Machine Learning with Applications A credit card fraud detection approach based on ensemble machine learning classifier with hybrid data sampling. Machine Learning with Applications, 20(April), 100675. https://doi.org/10.1016/j.mlwa.2025.100675

Li, J. (2025). Corporate governance, fraud learning cycles, and financial fraud detection: Evidence from Chinese listed firms. Research in International Business and Finance, 76(February), 102832. https://doi.org/10.1016/j.ribaf.2025.102832

Li, W., Liu, X., Su, J., & Cui, T. (2025). Advancing financial risk management: A transparent framework for effective fraud detection. Finance Research Letters, 75(February), 106865. https://doi.org/10.1016/j.frl.2025.106865

Liu, Z., & Kong, T. (2025). Evaluation of Enterprise Internal Control Based on Artificial Intelligence. Procedia Computer Science, 262, 1217–1227. https://doi.org/10.1016/j.procs.2025.05.163

Martin, K., Sanders, E., & Scalan, G. (2014). The potential impact of COSO internal control integrated framework revision on internal audit structured SOX work programs. Research in Accounting Regulation, 26(1), 110–117. https://doi.org/10.1016/j.racreg.2014.02.012

Musah, A., Padi, A., Blay, M. W., Okyere, D. O., & Ofori, B. S. (2025). Ethical organisational culture, effective internal control systems and tax compliance of small and medium scale enterprises (SMEs): The role of corporate governance. Social Sciences and Humanities Open, 11(January), 101331. https://doi.org/10.1016/j.ssaho.2025.101331

Rehman, A., Awan, K. A., Al-Rasheed, A., Ara, A., Alruwaili, F. F., Al-Otaibi, S., & Saba, T. (2025). A novel hybrid fuzzy logic and federated learning framework for enhancing cybersecurity and fraud detection in IoT-enabled metaverse transactions. Egyptian Informatics Journal, 30(April), 100668. https://doi.org/10.1016/j.eij.2025.100668

Shi, S., Luo, W., & Pau, G. (2025). An attention-based balanced variational autoencoder method for credit card fraud detection. Applied Soft Computing, 177(February), 113190. https://doi.org/10.1016/j.asoc.2025.113190

Stradling, J., Muhamadali, H., & Goodacre, R. (2024). Mobile guardians: Detection of food fraud with portable spectroscopy methods for enhanced food authenticity assurance. Vibrational Spectroscopy, 132(February), 103673. https://doi.org/10.1016/j.vibspec.2024.103673

Tubishat, M., Tbaishat, D., Al-Zoubi, A. M., Hraiz, A. E., & Habib, M. (2025). Leveraging evolutionary algorithms with a dynamic weighted search space approach for fraud detection in healthcare insurance claims. Knowledge-Based Systems, 317(November 2024), 113436. https://doi.org/10.1016/j.knosys.2025.113436

Wijaya, M. G., Pinaringgi, M. F., Zakiyyah, A. Y., & Meiliana. (2024). Comparative Analysis of Machine Learning Algorithms and Data Balancing Techniques for Credit Card Fraud Detection. Procedia Computer Science, 245(C), 677–688. https://doi.org/10.1016/j.procs.2024.10.294

Zhang, Y., Liu, T., & Li, W. (2024). Corporate fraud detection based on linguistic readability vector: Application to financial companies in China. International Review of Financial Analysis, 95(PB), 103405. https://doi.org/10.1016/j.irfa.2024.103405

Published

2026-04-08

How to Cite

Mudzakar, R. N., Karsam, K., & Paryanti, A. B. (2026). Analysis Effectiveness of the Internal Control System in Detecting Fraud (Study at PT Arminareka Perdana Group). Dinasti International Journal of Economics, Finance & Accounting, 7(1), 555–566. https://doi.org/10.38035/dijefa.v7i1.6420

Similar Articles

<< < 1 2 3 4 5 6 7 8 9 10 > >> 

You may also start an advanced similarity search for this article.