Musytari : Jurnal Manajemen, Akuntansi, dan Ekonomi

Vol. 25 No. 7 (2026): Musytari: Neraca Manajemen, Akuntansi, dan Ekonomi, ISSN 3025-9495
Articles

DAYA PREDIKTIF RASIO KEUANGAN DALAM MENDETEKSI KECURANGAN LAPORAN KEUANGAN PADA PERUSAHAAN PUBLIK INDONESIA

PDF
  • Mukhlis Purnama Hadi,
  • Erna Ratna Rahajeng,
  • Bambang Widagdo
Mukhlis Purnama Hadi
Universitas Muhammadiyah Malang
Erna Ratna Rahajeng
Universitas Muhammadiyah Malang
Bambang Widagdo
Universitas Muhammadiyah Malang
DOI: https://doi.org/10.2238/2y0db967

Published 2026-06-27

Keywords

  • Fraudulent Financial Reporting,
  • Financial Ratios,
  • Beneish M-Score,
  • Fraud Detection,
  • Indonesian Capital Market,
  • PSAK 72
    • ...More
    Less

How to Cite

DAYA PREDIKTIF RASIO KEUANGAN DALAM MENDETEKSI KECURANGAN LAPORAN KEUANGAN PADA PERUSAHAAN PUBLIK INDONESIA. (2026). Musytari : Jurnal Manajemen, Akuntansi, Dan Ekonomi, 25(7). https://doi.org/10.2238/2y0db967

Abstract

This study evaluates the predictive power of four financial ratio dimensions (profitability, liquidity, leverage, and activity) and two Beneish M-Score components in detecting fraudulent financial reporting (FFR) among Indonesian Stock Exchange issuers following PSAK 72 implementation. Using panel data from 250 non-financial firms over 2018–2023 (1,500 firm-year observations, 58 classified as manipulators), this study applies panel logistic regression with firm-fixed effects, supplemented by System GMM, IV-2SLS, and random forest robustness checks. Results show profitability ratios ROA (β = −4.876; p<0.001) and ROE (β = −3.421; p<0.001) as the most consistent FFR predictors, while Beneish components TATA and DSRI provide significant incremental discriminatory power. Liquidity ratios are non-significant and potentially misleading due to receivables manipulation (liquidity paradox). The model achieves an AUC-ROC of 0.841, exceeding single-ratio models (AUC: 0.65–0.72). This study represents one of the early investigations testing financial ratio effectiveness in the post-PSAK 72 Indonesian capital market using a fixed-effects panel design.
PDF

References

  1. Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589–609. https://doi.org/10.1111/j.1540-6261.1968.tb00843.x
  2. Arellano, M., & Bover, O. (1995). Another look at the instrumental variable estimation of error-components models. Journal of Econometrics, 68(1), 29–51. https://doi.org/10.1016/0304-4076(94)01642-D
  3. Association of Certified Fraud Examiners. (2024). Report to the nations: 2024 global study on occupational fraud and abuse. ACFE.
  4. 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
  5. Bertomeu, J., Cheynel, E., Floyd, E., & Pan, W. (2021). Using machine learning to detect misstatements. Review of Accounting Studies, 26(2), 468–519. https://doi.org/10.1007/s11142-020-09563-8
  6. Blundell, R., & Bond, S. (1998). Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics, 87(1), 115–143. https://doi.org/10.1016/S0304-4076(98)00009-8
  7. Chen, Y., Kelly, B., & Wu, W. (2022). Sophisticated investors and market efficiency: Evidence from a natural experiment. Journal of Financial Economics, 144(1), 1–26. https://doi.org/10.1016/j.jfineco.2021.06.003
  8. Committee of Sponsoring Organizations of the Treadway Commission (COSO). (2023). Fraud risk management guide (2nd ed.). COSO.
  9. Cressey, D. R. (1953). Other people's money: A study in the social psychology of embezzlement. Free Press.
  10. Dechow, P. M., & Dichev, I. D. (2002). The quality of accruals and earnings: The role of accrual estimation errors. The Accounting Review, 77(s-1), 35–59. https://doi.org/10.2308/accr.2002.77.s-1.35
  11. 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
  12. Dyck, A., Morse, A., & Zingales, L. (2024). How pervasive is corporate fraud? Review of Accounting Studies, 29(2), 736–769. https://doi.org/10.1007/s11142-022-09738-5
  13. Hosmer, D. W., & Lemeshow, S. (2013). Applied logistic regression (3rd ed.). Wiley.
  14. Huang, A. H., Wang, H., & Yang, Y. (2022). FinBERT: A pretrained language model for financial communications. Management Science, 68(8), 6073–6100. https://doi.org/10.1287/mnsc.2022.4310
  15. Jensen, M. C., & Meckling, W. H. (1976). Theory of the firm: Managerial behavior, agency costs and ownership structure. Journal of Financial Economics, 3(4), 305–360. https://doi.org/10.1016/0304-405X(76)90026-X
  16. Lennox, C. S., & Pittman, J. A. (2010). Big Five audits and accounting fraud. Contemporary Accounting Research, 27(1), 209–247. https://doi.org/10.1111/j.1911-3846.2010.01007.x
  17. Loughran, T., & McDonald, B. (2011). When is a liability not a liability? Textual analysis, dictionaries, and 10-Ks. The Journal of Finance, 66(1), 35–65. https://doi.org/10.1111/j.1540-6261.2010.01625.x
  18. Manski, C. F. (2003). Partial identification of probability distributions. Springer.
  19. Ngai, E. W. T., Hu, Y., Wong, Y. H., Chen, Y., & Sun, X. (2020). The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature. Decision Support Systems, 50(3), 559–569. https://doi.org/10.1016/j.dss.2010.08.006
  20. Orji, I. J., & Ukpabi, D. (2022). A two-stage hybrid model for financial statement fraud detection in emerging economies. Journal of Financial Crime, 29(3), 1001–1022. https://doi.org/10.1108/JFC-07-2021-0162
  21. Otoritas Jasa Keuangan. (2023). Laporan tahunan pengawasan pasar modal dan keuangan 2022. OJK Republik Indonesia.
  22. Pan, Y., Pikulina, E., Siegel, S., & Wang, T. Y. (2022). Do analysts' target price forecasts reflect industry information? The Accounting Review, 97(2), 1–22. https://doi.org/10.2308/TAR-2019-0049
  23. Perols, J. L. (2011). Financial statement fraud detection: An analysis of statistical and machine learning algorithms. Auditing: A Journal of Practice & Theory, 30(2), 19–50. https://doi.org/10.2308/ajpt-50009
  24. Roodman, D. (2009). How to do xtabond2: An introduction to difference and system GMM in Stata. The Stata Journal, 9(1), 86–136. https://doi.org/10.1177/1536867X0900900106
  25. Skousen, C. J., Smith, K. R., & Wright, C. J. (2009). Detecting and predicting financial statement fraud: The effectiveness of the fraud triangle and SAS No. 99. Advances in Financial Economics, 13, 53–81. https://doi.org/10.1108/S1569-3732(2009)0000013005
  26. Spathis, C. T. (2002). Detecting false financial statements using published data: Some evidence from Greece. Managerial Auditing Journal, 17(4), 179–191. https://doi.org/10.1108/02686900210424321
  27. Summers, S. L., & Sweeney, J. T. (1998). Fraudulently misstated financial statements and insider trading: An empirical analysis. The Accounting Review, 73(1), 131–146.
  28. Tarjo, & Herawati, N. (2015). Application of Beneish M-Score models and data mining to detect financial fraud. Procedia - Social and Behavioral Sciences, 211, 924–930. https://doi.org/10.1016/j.sbspro.2015.11.122
  29. Vousinas, G. L. (2019). Advancing theory of fraud: The S.C.O.R.E. model. Journal of Financial Crime, 26(1), 372–381. https://doi.org/10.1108/JFC-12-2017-0128
  30. Wang, T. Y. (2013). Corporate securities fraud: Insights from a new empirical framework. Journal of Law, Economics, and Organization, 29(3), 535–568. https://doi.org/10.1093/jleo/ewr009
  31. Windmeijer, F. (2005). A finite sample correction for the variance of linear efficient two-step GMM estimators. Journal of Econometrics, 126(1), 25–51. https://doi.org/10.1016/j.jeconom.2004.02.005

Similar Articles

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