FRAUDULENT FINANCIAL REPORTING DETECTION USING BENEISH M-SCORE MODEL IN PUBLIC COMPANIES IN 2012-2016

fhiqi alfian, NiNyoman Triani

Abstract


This study aims to examine the ability of the Beneish M-Score model to detect fraudulent financial reporting. The Data research sample consisted of 55 manipulator companies that were sanctioned and fined by the Financial Services Authority due to violating POJK number X.K.1; X.K.4; IX.E.1; IX.E.2 and VIII.G.7 during 2012-2016 and 55 non-manipulator companies as comparison companies. The results showed that 28 of the 55 manipulator companies were correctly classified as fraud firms by 50.91%, while for the category of non-manipulator companies, there were as many as 60% or 33 of the 55 companies correctly classified as non-fraud firms.The results also identified three M-Score variables that were often manipulated by manipulator and non-manipulator companies namely Sales and General and Administrative Expenses Index (SGAI), Depreciation Index (DEPI) and Asset Quality Index (AQI), but two other variables namely Days Sales in Receivables Index (DSRI) and Sales Growth Index (SGI) are considered unable to detect fraudulent financial reporting. In addition, based on the results of the logistic regression test, the eight Beneish M-Score variables have no effect on the detection of fraudulent financial reporting. The Beneish M-Score model is an easy and cheap way to detect possible fraudulent financial reporting, this can be applied by investors before determining their investment decisions.

Keywords


Fraudulent financial reporting; Beneish M-Score; Logistic Regression

Full Text:

PDF

References


Association of Certified Fraud Examiners. (2016). Report to the nations on occupational fraud and abuse. Association of Certified Fraud Examiners, 1–92.

Association of Certified Fraud Examiners. (2016). Survai Fraud Indonesia. Association of Certified Fraud Examiners, 1–62.

Beneish, M. D. (1999). The Detection of Earnings Manipulation.

Beneish, M. D., Lee, C. M. C., & Nichols, D. C. (2012). Fraud Detection and Expected Returns.

Beneish, M. D., & Nichols, D. C. (2007). The Predictable Cost of Earnings Manipulation.

Erickson, M., Hanlon, M., & Maydew, E. (2004). Was There a Link Between Executive Compensation and Accounting Fraud?

Ghozali, I. (2016). Aplikasi Analisis Multivariete Dengan Program IBM SPSS 23. (Prayogo P Harto, Ed.) (8th ed.). Semarang: Badan Penerbit Universitas Diponegoro.

Gujarati, D. N., & Porter, D. C. (2010). Dasar-Dasar Ekonometrika (5th ed.). Jakarta: Salemba Empat.

Hasan, M. S., Omar, N., Barnes, P., & Handley-Schachler, M. (2017). A cross-country study on manipulations in fi nancial statements of lwasted companies Evidence from Asia. Journal of Financial Crime, 24(4), 656–677. https://doi.org/10.1108/JFC-07-2016-0047

Healy, P. M., & Wahlen, J. M. (1999). A review of the earnings management literature and its implications for standard setting. Accounting Horizons.

Holthausen, R. W., & Leftwich, R. W. (1983). The economics consequences of accounting choice. Journal of Accounting and Economics, 5(2), 77–117.

Ikatan Akuntan Publik Indonesia. Standar Audit 240 Tentang Tanggung Jawab Auditor Terkait dengan Kecurangan dalam Suatu Audit atas Laporan Keuangan (2015). Indonesia.

Kamal, M. E. M., Salleh, M. F. M., & Ahmad, A. (2016). Detecting Financial Statement Fraud by Malaysian Public Lwasted Companies: The Reliability of the Beneish M-Score Model. Jurnal Pengurusan, 46, 23–32.

Ramírez-Orellana, A., Martínez-Romero, M. J., & Marino-Garrido, T. (2017). Measuring fraud and earnings management by a case of study: Evidence from an international family business. European Journal of Family Business (2017), XXX, 1–13. https://doi.org/10.1016/j.ejfb.2017.10.001

Sarwono, J. (2006). Metode Penelitian Kuantitatif dan Kualitatif. Yogyakarta: Graha Ilmu.

Sekaran, U., & Bougie, R. (2013). Research Methods For Business. United Kingdom: John Wiley & Sons, Inc.

Setiawan, B. (2015). Teknik Praktik Analwaswas Data Penelitian Sosial dan Bwasnwas Dengan SPSS. (Nikodemus, Ed.) (1st ed.). Yogyakarta: Andi Yogyakarta.

Suwardjono. (2006). Teori Akuntansi Perekayasaan Pelaporan Keuangan. Edisi Ketiga. Yogyakarta : BPFE : Yogyakarta.

Sugiyono. (2017). Metode Penelitian Kuantitatif, Kualitatif, Dan R&D (26th ed.). Bandung: Alfabeta CV.

Tarjo, & Herawati, N. (2015). Application of Beneish M-Score Models and Data Mining to Detect Financial Fraud. Procedia - Social and Behavioral Sciences, 211(September), 924–930. https://doi.org/10.1016/j.sbspro.2015.11.122

Teoh, S. H., Welch, I., & Wong, T. J. (1998). Earnings management and the long-term under performance of initial public offerings. Journal of Finance, 53, 1935–1974.

Tuanakotta, T. M. (2010). Akuntansi Forensik Dan Audit Investigatif. (T. E. S. Empat, Ed.) (2nd ed.). Salemba Empat.

Tuanakotta, T. M. (2013). Mendeteksi Manipulasi Laporan Keuangan. (E. S. Suharsi, Ed.). Jakarta: Salemba Empat.

Tuanakotta, T. M. (2015). Audit kontemporer. (E. S. Suharsi, Ed.). Jakarta: Salemba Empat.

Widarjono, A. (2015). Analwaswas Multivariat Terapan Dengan Program SPSS, AMOS, dan Smartplas. Yogyakarta: UPP STIM YKPN.


Refbacks

  • There are currently no refbacks.


Copyright (c) 2019 Asia Pacific Fraud Journal

Asia Pacific Fraud Journal (APFJ) Journal indexed in

Image result for ccbysa

Copyright @ 2016 Association of Certified Fraud Examiners Indonesia Chapter