The Ability of the Beneish M-Score To Detect the Trends of Fraud in the Indonesian Sharia Stock Index
1Risky Mezi Muria, 2Mohammad Nizarul Alim, 3Prasetyono
1,2,3Trunojoyo Madura University, Bangkalan, Indonesia
https://doi.org/10.47191/jefms/v7-i1-76ABSTRACT:
The purpose of this study is to determine the level of ability of the Beneish M-Score in detecting financial statement fraud tendencies. This study uses a quantitative approach with logistic regression analysis techniques. The results of this study provide empirical evidence that is different from research on manufacturing companies, state-owned companies, banking, mining, non-financial companies, property companies, real estate and building construction. The contribution of the empirical results of this study shows that overall the Beneish M-Score model is unable to detect the tendency of fraudulent financial statements in Islamic companies. DSRI, GMI, AQI, SGI, DEPI, SGAI, LVGI, and TATA has no effect on fraudulent financial statements in sharia companies.
KEYWORDS:
Beneish M-Score, Fraud, Financial Statement Fraud, Fraud Detection, Sharia Company.
REFERENCES:
1) K. I. P. Fernanda, “Deteksi Financial Statement Fraud Dengan Model Beneish M-Score,” J. Akunt. Unesa, vol. 5, no. 1, pp. 1–22, 2016.
2) P. Harsanti and U. R. Mulyani, “Testing of Fraudulent Financial Statements With the Beneish M-Score Model for Manufacturing Companies Listed in the Indonesian Stock Exchange,” Acad. Int. Conf. Lit. Nov. KnE, pp. 125–133, 2021, doi: 10.18502/kss.v5i7.9328.
3) V. Suheni and M. F. Arif, “Mendeteksi financial statement fraud dengan menggunakan Model Beneish M-score (studi pada perusahaan sektor manufaktur yang terdaftar di bursa efek Indonesia),” J. Akunt. Ekon. FE UN PGRI Kediri Vol 5 No 2, Juli 2020, vol. 5, no. 2, pp. 92–99, 2020.
4) S. Santosa and J. Ginting, “Evaluasi Keakuratan Model Beneish M-Score Sebagai Alat Deteksi Kecurangan Laporan Keuangan (Kasus Perusahaan Pada Otoritas Jasa Keuangan di Indonesia),” Maj. Ilm. Bijak, vol. 16, no. 2, pp. 75–84, 2019, doi: 10.31334/bijak.v16i2.508.
5) Y. Fadilah, Maslichah, and M. C. Mawardi, “Penerapan model beneish m-score dan analisis rasio untuk mendeteksi kecurangan laporan keuangan (studi empiris pada perusahaan yang mendapat suspend dari BEI tahun 2018),” E-Jra, vol. 08, no. 01, pp. 1–13, 2019.
6) N. Omar, R. K. Koya, Z. M. Sanusi, and N. A. Shafie, “Financial Statement Fraud: A Case Examination Using Beneish Model and Ratio Analysis,” Int. J. Trade, Econ. Financ., vol. 5, no. 2, pp. 184–186, 2014, doi: 10.7763/ijtef.2014.v5.367.
7) M. L. Roxas, “Financial Statement Fraud Detection Using Ratio and Digital Analysis,” J. Leadership, Account. Ethics, vol. 8, no. 99, pp. 56–66, 2011, [Online]. Available: http://search.proquest.com.ezaccess.library.uitm.edu.my/docview/909953374?accountid=42518.
8) Tarjo and N. Herawati, “Application of Beneish M-Score Models and Data Mining to Detect Financial Fraud,” Procedia - Soc. Behav. Sci., vol. 211, no. September, pp. 924–930, 2015, doi: 10.1016/j.sbspro.2015.11.122.
9) T. Ahmed and J. Naima, “Detection and analysis of probable earnings manipulation by firms in a developing country,” Asian J. Bus. Account., vol. 9, no. 1, pp. 59–81, 2016.
10) R. Septiani, S. Musyarofah, and R. Yuliana, “Beneish M-Score Reliability as a Tool For Detecting Financial Statements Fraud,” Int. Colloq. Forensics Account. Gov., vol. 1, no. 1, pp. 140–149, 2020.
11) S. N. Nyakarimi, S. N. Kariuki, and P. Wang’ombe Kariuki, “Financial Statements Manipulations Using Beneish Model and Probit Regression Model: A Case of Banking Sector in Kenya,” Eur. Online J. Nat. Soc. Sci., vol. 9, no. 1, pp. 253–264, 2020, [Online]. Available: http://www.european-science.com.
12) Hantono, “Deteksi Financial Statement Fraud Melalui Model Beneish Pada Perusahaan Bumb,” JMBI UNSRAT (Jurnal Ilm. Manaj. Bisnis dan Inov. Univ. Sam Ratulangi)., vol. 5, no. 3, pp. 135–150, 2018, doi: 10.35794/jmbi.v5i3.21705.
13) I. Irsutami and R. Sapriadi, “Mendeteksi Kecurangan Laporan Keuangan Menggunakan Model Beneish,” J. Appl. Account. Tax., vol. 5, no. 1, pp. 36–49, 2020, doi: 10.30871/jaat.v5i1.1868.
14) S. Repousis, “Using Beneish model to detect corporate financial statement fraud in Greece,” J. Financ. Crime, vol. 23, no. 4, pp. 1063–1073, 2016, doi: 10.1108/JFC-11-2014-0055.
15) ACFE, “Report To The Nations - Global Study on Occupational Fraud and Abuse: Asia Pacific,” Asia Pacific Ed., vol. 10, p. 80, 2018.
16) ACFE, “Report To The Nation Asia Pacific Edition 2020,” no. August, pp. 1–16, 2020.
17) ACFE, “Survei Fraud Indonesia,” Assoc. Certif. Fraud Exam., pp. 7–10, 2016, doi: 10.1201/9781315178141-3.
18) PwC, “PwC’s Global Economic Crime and Fraud Survey 2020,” 2020. https://www.pwc.com/fraudsurvey.
19) K. Zvarikova and E. Kovalova, “Could globally used Beneish M-score predict the manipulation of the accounting statements in the Slovak republic ?,” vol. 03037, 2021.
20) M. D. Beneish, “The Detection of Earnings Manipulation,” Source Financ. Anal. J., vol. 55, no. 5, pp. 24–36, 1999.
21) A. Widodo and R. Yusiana, “How E-Marketing And Trust Influence Online Buying Decision: A Case Study Of Mataharimall.Com In Bandung,” Soc. Sci. Humanit., no. November 2019, 2017, doi: 10.13140/RG.2.2.16459.95520.
22) J. Hugo, “Efektivitas Model Beneish M-Score Dan Model F-Score Dalam Mendeteksi Kecurangan Laporan Keuangan,” vol. 3, no. 1, pp. 165–175, 2019.
23) A. Özcan, “The Use of Beneish Model in Forensic Accounting Accunting: Evidence from Turkey,” J. Appl. Econ. Bus. Res., vol. 8, no. 1, pp. 57–67, 2018, [Online]. Available: http://eds.a.ebscohost.com/eds/detail/detail?vid=2&sid=1fbdf147-6e2c-4a2b-8078-bb63d62d6ab0%40sessionmgr4007&bdata=JnNpdGU9ZWRzLWxpdmUmc2NvcGU9c2l0ZQ%3D%3D#AN=128868562&db=bth.
24) N. Lotfi and A. A. Chadegani, “Detecting corporate financial fraud using Beneish M-score model,” Int. J. Financ. Manag. Account., vol. 2, no. 8, pp. 29–34, 2017.
25) Dewan Komisioner Otoritas Jasa Keuangan, “Otoritas jasa keuangan republik indonesia,” 2015.
26) S. A. Hassairi and W. Rajhi, “Unconventional Banking System in Distress,” vol. 3, no. 4, pp. 70–82, 2011, doi: 10.5539/ijef.v3n4p70.
27) C. C. Okeahalam, “The Political Economy of Bank Failure and Supervision in the Republic of South Africa,” African Assoc. Polit. Sci., vol. Vol.3 No.2, 1998.
28) OJK, “Statistik Pasar Modal Syariah Per Desember 2021 Perkembangan Saham Syariah,” pp. 1–2, 2021.
29) M. Spence, “Job Market Signaling,” vol. 87, no. 3, pp. 355–374, 1973.
30) Brigham and Huston, Dasar-Dasar Manajemen Keuangan, 2nd ed. Jakarta: Selemba Empat, 2011.
31) T. A. Gumanti, “Teori Sinyal Dalam Manajemen Keuangan,” Manaj. dan Usahaw. Indones., no. September, pp. 1–29, 2009.
32) [32] Elder, Jasa Audit dan Assurance Pendekatan Terpadu (Adaptasi Indonesia), 1st ed. Jakarta: Penerbit Salemba Empat., 2011.
33) S. L. Summers and J. T. Sweeney, “Fraudulently misstated financial statements and insider trading : An empirica ...,” 1998.
34) V. Petrik, “Application of Beneish M-Score on Selected,” Vysok. škola manažmentu, Panónska cesta 17, 851 04 Bratislava, no. December, pp. 2–7, 2016.
35) R. Arshad, S. M. Iqbal, and N. Omar, “Prediction of business failure and fraudulent financial reporting: Evidence from Malaysia,” Indian J. Corp. Gov., vol. 8, no. 1, pp. 34–53, 2015, doi: 10.1177/0974686215574424.
36) B. Lev and S. R. Thiagarajan, “Accounting Research Center, Booth School of Business, University of Chicago,” J. Account. Res., vol. 31, no. 2, pp. 190–215, 1993.
37) N. A. Aris, S. M. M. Arif, M. Arif, R. Othman, and M. M. Zain, “Fraudulent Financial Statement Detection Using Statistical Techniques: The Case Of Small Medium Automotive Enterprise,” J. Appl. Bus. Res., vol. 31, no. 4, pp. 1469–1478, 2015.
38) AICPA, “Consideration of Fraud in a Financial,” pp. 1719–1770, 2002.
39) C. J. Skousen, K. R. Smith, and C. J. Wright, Article information :, no. 99. 2015.
40) G. Bhavani and C. T. Amponsah, “M-Score and Z-Sore for Detection of Accounting Fraud,” Account. Bus. Public Interes., pp. 68–86, 2017.
41) O. S. Persons, “Using Financial Information to Differentiate Failed vs . Surviving Finance Companies in Thailand : An Implication for Emerging Economies *,” vol. 3, no. 2, pp. 127–145, 1997, doi: 10.17578/3-2-3.
42) S. R. William, Financial Accounting Theory, 7th ed. United States: Pearson Education, 2015.
43) N. V. Feruleva and M. A. Shtefan, “Detecting Financial Statements Fraud: the Evidence from Russia,” J. Corp. Financ. Res. / Корпоративные Финансы | ISSN 2073-0438, vol. 11, no. 2, pp. 32–45, 2017, doi: 10.17323/j.jcfr.2073-0438.11.2.2017.32-45.
44) Y. Lou and M. Wang, “Fraud Risk Factor Of The Fraud Triangle Assessing The Likelihood Of Fraudulent Financial Reporting,” vol. 7, no. 2, pp. 61–78, 2009.
45) P. M. Dechow, W. Ge, C. R. Larson, and R. G. Sloan, “Predicting Material Accounting Misstatements,” Contemp. Account. Res. Vol., vol. 28, no. 1, pp. 17–82, 2011, doi: 10.1111/j.1911-3846.2010.01041.x.
46) P. M. Dechow and I. D. Dichev, “The Quality of Accruals and Earnings: The Role of Accrual Estimation Errors.” University of Michigan Business School, 2002.
47) K. L. Jones, G. V. Krishnan, and K. D. Melendrez, “Do Models of Discretionary Accruals Detect Actual Cases of Fraudulent and Restated Earnings ? An Empirical Analysis,” vol. 25, no. 2, pp. 499–531, 2008, doi: 10.1506/car.25.2.8.
48) M. F. Mcnichols, “The Quality of Accruals and Earnings : The Role of Accrual Estimation Errors,” vol. 77, pp. 61–69, 2002.
49) Hery, Auditing dan Asurans. Jakarta, 2016.
50) Suwardjono, Teori Akuntansi Perekayasaan Pelaporan Keuangan, 3rd ed. Yogyakarta: BPFE, 2006.
51) F. Handayani and Fuad, “Faktor yang Berpengaruh Terhadap Perataan Laba Perusahaan Otomotif yang Terdaftar di Bursa Efek Indonesia (BEI) Periode 2009-2012,” Diponegoro J. Account., vol. 4, no. 2, p. hal. 1-12, 2015.
52) R. A. Annisa and I. Ghozali, “Pendeteksian Kecurangan Laporan Keuangan Menggunakan Analisis Beneish M-Score Pada Perusahaan Non Keuangan Yang Terdaftar Di Bursa Efek Indonesia Tahun 2017-2018,” Diponegero J. Account., vol. 9, pp. 1–12, 2020.
