Detecting Financial Statement Fraud in Indonesian Companies: The Beneish M-Score Model

Authors

  • Ghazi Althof Ayyasy Mataram University
  • Isnawati Mataram University

DOI:

https://doi.org/10.35129/simak.v24i01.667

Keywords:

Financial Statement Fraud, Beneish M-Score, Profit Manipulation, Corporate Governance, Indonesia Stock Exchange

Abstract

This study aims to detect financial statement fraud in Indonesian companies using the Beneish M-Score model. The research covers 622 companies from ten sectors listed on the Indonesia Stock Exchange (IDX) during the period 2021–2024, with a total of 2,488 financial statements. The data was obtained from the IDX's published financial reports and analysed using eight Beneish M-Score ratios to classify companies into manipulator and non-manipulator categories. The results show that 34.03% of companies were identified as manipulators, while the remaining 65.97% were classified as non-manipulators. The technology sector had the highest percentage of manipulators (45%), followed by the raw materials sector (37%) and energy (36.49%). Conversely, the transportation (24.04%) and health (27.68%) sectors had the lowest percentages. These findings confirm that financial statement fraud remains a serious problem, with varying levels of risk across sectors. The implications of the study emphasise the importance of strengthening corporate governance, the role of external audits, and regulatory oversight to prevent manipulative practices. In addition, this study contributes to academic literature by providing a comprehensive overview of cross-sector manipulation trends in Indonesia.

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References

ACFE. (2016). Report to the nations on occupational fraud and abuse 2016. Report to the Nations, 1–92.

ACFE. (2024). Association of Certified Fraud Examiners The Nations Occupational Fraud 2024 :A Report To The Nations. Association of Certified Fraud Examiners, 1–106.

Albrecht, W. S., Albrecht, C. C., & Albrecht, Chad O. Zimbelman, M. F. (2016). Fraud Examination (5th ed.). Cengage Learning.

Altman, E. I. (1968). Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy. Journal of Finance, 23(4), 589–609. https://doi.org/http://dx.doi.org/10.1111/j.1540-6261.1968.tb00843.x

Aros, L. H., Molano, L. X. B., Gutierrez-Portela, F., Hernandez, J. J. M., & Barrero, M. S. R. (2024). Financial fraud detection through the application of machine learning techniques: a literature review. Humanities and Social Sciences Communications, 11(1), 1–22. https://doi.org/10.1057/s41599-024-03606-0

Beneish, M. D. (1999). The Detection of Earnings Manipulation. Financial Analysts Journal, 55(5), 24–36. https://doi.org/10.2469/faj.v55.n5.2296

Creswell, J. W. (2014). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (4th ed.). SAGE Publications, Inc.

Dechow, P. M., Ge, W., Larson, C. R., & Sloan, R. G. (2011). Predicting Material Accounting Misstatements. Contemporary Accounting Research, 28(1), 17–82. https://doi.org/10.1111/j.1911-3846.2010.01041.x

Ginting, D. B., & Daljono. (2023). Analisis Pengaruh Fraud Hexagon Terhadap Fraudulent Financial Reporting Menggunakan Metode Beneish M-Score (Studi Empiris pada Sektor Industri Barang Konsumsi yang Terdaftar di Bursa Efek Indonesia Tahun 2019-2021). Diponegoro Journal of Accounting, 12(3), 1–15. http://ejournal-s1.undip.ac.id/index.php/accounting

Hayuningrum, R. R. S., & Sari, S. P. (2023). Kajian Mekanisme Corporate Governance Terhadap Integritas Laporan Keuangan. Jurnal Akuntansi Dan Pajak, 23(2), 1–8.

Hugo, J. (2019). Efektivitas Model Beneish M-Score Dan Model F-Score Dalam Mendeteksi Kecurangan Laporan Keuangan. Jurnal Muara Ilmu Ekonomi Dan Bisnis, 3(1), 165. https://doi.org/10.24912/jmieb.v3i1.2296

Jensen, M. C., & Meckling, W. H. (1976). Theory of the Firm: Managerial Behavior, Agency Costs an Ownership Structure. Journal of Financial Economics, 3, 305–360. https://doi.org/http://dx.doi.org/10.1016/0304-405X(76)90026-X

Khatun, A., Ghosh, R., & Kabir, S. (2022). Earnings manipulation behavior in the banking industry of Bangladesh: the strategical implication of Beneish M-score model. Arab Gulf Journal of Scientific Research, 40(3), 302–328. https://doi.org/10.1108/AGJSR-03-2022-0001

Lasdin, M. S., & Ratnawati, T. (2023). Pengaruh Kepatuhan Sak, Salah Saji Material, Reputasi Kap Terhadap Financial Distress Dan Opini Audit Pada Perusahaan Manufaktur Yang Terdapat Di Bursa Efek Indonesia (Bei) Tahun 2019-2020. Inisiatif: Jurnal Ekonomi, Akuntansi Dan Manajemen, 2(4), 19–32. https://doi.org/10.30640/inisiatif.v2i4.1405

MacCarthy, J. (2017). Using Beneish M-Score and Altman Z-Score Models to Detect Financial Fraud and Company Failure: A Case Study of Enron Corporation. International Journal of Finance and Accounting, 6(6), 159–166. https://doi.org/10.5937/tekstind2104020k

Monica, N., Putri, A., Afrilyani, R., & Haryanti, R. (2023). Analisis Kecurangan Laporan Keuangan PT Asuransi Jiwasraya dengan Analisis Fraud Pentagon. Sanskara Akuntansi Dan Keuangan, 1(02), 92–99. https://doi.org/10.58812/sak.v1i02.70

Nugraha, A. G., Hakimah, A. D., & Lia, U. (2024). Analysis of Utility Approaches in Accounting Reporting Decision Making. International Journal of Economics and Management Sciences, 2(1), 22–31. https://doi.org/10.61132/ijems.v2i1.391

Nurjanah, N., Widagdo, R., & Rokhlinasari, S. (2023). Ratio Index Beneish M-Score dalam Mendeteksi Kecurangan pada Laporan Keuangan Perusahaan Asuransi yang Terdaftar di BEI Periode 2018-2020. Journal of Sharia Accounting and Tax, 1(2), 209–222. https://doi.org/10.24235/jsat.v1i2.22

Patmawati, P., & Rahmawati, M. (2023). Deteksi Financial Statement Fraud : Model Beneish M-Score, dan Model F-Score. E-Jurnal Akuntansi, 33(1), 34. https://doi.org/10.24843/eja.2023.v33.i01.p03

Ramadhan, Y., Darmatika, W., Arzeta, D., Isnaeni, W., Syukriati, S., & Caprina, T. (2025). Analisis Kecurangan Laporan Keuangan Dengan Model Beneish M-Score Pada Sektor Kesehatan. Jurnal Kajian Ekonomi & Bisnis Islam, 6(9), 2808–2818.

Rezaee, Z. (2010). Financial Statement Fraud: Prevention and Detection. In Financial Statement Fraud: Prevention and Detection, Second Edition (2nd ed.). John Wiley & Sons, Inc.

Scott, W. R. (2015). Financial Accounting Theory (7th ed.). Pearson Canada Inc.

Sudarma, M., & Wulandari, P. P. (2025). Kegunaan Keputusan Informasi Akuntansi (Decision Usefulness) Dari Perspektif Investor Bursa Efek Indonesia: Sebuah Studi Fenomenologi. Jurnal KRISNA: Kumpulan Riset Akuntansi, 16(2), 2025–2386. https://doi.org/10.22225/kr.16.2.2025.386-402

Tio, S., & Kuswanto, R. (2025). Integritas Laporan Keuangan di Sektor Perbankan Indonesia : Peran Komite Audit , Good Corporate Governance , dan Kualitas Audit dalam Mencegah Kecurangan Abstrak. JEMSI (Jurnal Ekonomi, Manajemen, Dan Akuntansi), 11(4), 2344–2355.

Warseno, W., Sulistyaningsih, S., & Rafika, A. S. (2023). Pendekteksian Kecurangan Laporan Keuangan Menggunakan Beneish M-Score Model Pada Perusahaan Perbankan Periode 2014-2018. Ijacc, 4(1), 29–40. https://doi.org/10.33050/ijacc.v4i1.2670

Wells, J. T. (2017). Principles of Fraud Examination (4th ed.). John Wiley & Sons, Inc.

Widowati, A. I., & Oktoriza, L. A. (2021). Pendeteksian Kecurangan Laporan Keuangan Dengan Beneish M-Score Pada Perusahaan Yang Terdaftar Di Bursa Efek Indonesia. SOLUSI : Jurnal Ilmiah Bidang Ilmu Ekonomi, 19(1), 1–11. https://journals.usm.ac.id/index.php/solusi/article/view/2994

Zhou, Y., Xiao, Z., Gao, R., & Wang, C. (2024). Using data-driven methods to detect financial statement fraud in the real scenario. International Journal of Accounting Information Systems, 54. https://doi.org/10.1016/j.accinf.2024.100693

Additional Files

Published

05/31/2026

How to Cite

Detecting Financial Statement Fraud in Indonesian Companies: The Beneish M-Score Model. (2026). SIMAK, 24(01). https://doi.org/10.35129/simak.v24i01.667

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