Authors:

Patmawati Patmawati, Meita Rahmawati

Abstract:

“The research aims to find out which model is most effective in detecting financial statement fraud by using the Beneish M-Score model and the F-Score model. The research population of the banking sector is listed on the Indonesia Stock Exchange for the period 2018 - 2020 with a total of 48 banks. The research sample totaled 40 banks using a purposive sampling method. The results of the study stated that by calculating the index using the beneficial M-Score model, it showed that there were indications of banks committing financial statement fraud in 2018 at 2.5%, in 2019 at 95% and in 2020 at 97.5%. Meanwhile, using the F-Score model in 2018 it was 5%, in 2019 it was 7.5% and in 2020 it was 5%. Based on these two models, an effective model for detecting financial statement fraud is the Beneish M-Score model. Keywords: Financial Statement Fraud; Model Beneish M-Score; Model F-Score.”

Keywords

Financial Statement Fraud; Model Beneish M-Score; Model F-Score.

Downloads:

Download data is not yet available.

References

  • ACFE. (2016). The Fraud Tree Occupational Fraud And Abuse Classification System. 2016 Ed, Austin, Texas.
  • ACFE. (2018). Report To The Nation Association of Certified Fraud Examiners. Report to the Nation (RTTN). https://www.fraud-magazine.com/article.aspx?id=4295001895
  • ACFE. (2022). Fraud 101: What Is Fraud? Association of Certified Fraud Examiners. https://www.acfe.com/fraud-resources/fraud-101-what-is-fraud
  • Alpeyev, P. dan Amano, T. (2015). “Toshiba to restate at least 152 billion Yen of past profits.” Bloomberg. https://www.bloomberg.com/news/articles/2015-07-20/toshiba-to-restate-152-billion-yen-of-past-profits-after-probe
  • Aris, N. A., Arif, S.M.M., Othman, R., dan Zain, M. M. (2015). Fraudulent financial statement detection using statistical techniques: The case of small medium automotive enterprise. Journal of Applied Business Research, 4(31), 1469.
  • Balachandran, S. . (2009). The Satyam scandal”. Forbes. https://www.forbes.com/2009/01/07/satyam-raju-governance-oped-cx_sb_0107balachandran.html?sh=7fb9dd4b3044
  • Beneish. (1999). The Detection of Earning Manipulation. Financial Analysis Journal, Vol. 55, Pages 24-36.
  • Beneish. (2012). Fraud Detection and Expected Return. http://papers.ssrn.com/ abstract_id=1998387
  • Bloomberg. (2001). The fall of Enron. Bloomberg. https://www.bloomberg.com/news/articles/2001-12-16/the-fall-of-enron?leadSource=uverify wall
  • Cecchini., Aytug, Koehler, dan P. (2010). Making words work: Using financial text as a predictor of financial events. Decision Support Systems, 50, 164–175. https://doi.org/https://doi.org/10.1016/j.dss.2010.07.012
  • Dechow, P.M., Ge, W., Larson, C.R., dan Sloan, R. . (2011). Predicting material accounting misstatements. Contemporary Accounting, 1(28), 17–82.
  • Detik, F. (2009). “Usai manipulasi keuangan”. Waskita Karya segera direstrukturisasi. Finance Detik. https://finance.detik.com/berita-ekonomi-bisnis/d-1200038/usai-manipulasi-keuangan-waskita-karya-segera-direstukturisasi
  • Hantono. (2018). Analisis Pendeteksian Financial Statement Fraud Dengan Pendekatan Model Beneish Pada Perusahaan Bumn Going Concern. Jurnal Riset Akuntansi, 3(13), 254–269. https://doi.org/https://doi.org/10.32400/gc.13.03.20170.2018
  • Herry. (2016). Auditing dan Asuransi. Jakarta : PT Grasindo.
  • Jensen, M. (1976). Theory of the firm : Managerial behavior, agency costs and owneship structure. Journal of Financial Economics, 3(4), 305–360.
  • Priantara, D. (2013). Fraud Auditing and Investigation (Jakarta (ed.)). Mitra Wacana Media.
  • Repousis, S. (2016). Using Beneish model to detect corporate financial statement fraud in Greece. Journal of Financial Crime, 4(23), 1063 – 1073.
  • Scott, W. R. (2015). Financial accounting theory (7th ed). Pearson Education.
  • Singleton, T.W. dan Singleton, A. J. (2010). Fraud auditing and forensic accounting (N. Jersey (ed.); 4th ed.). John Wiley & Sons.
  • Skousen. (2009). Detecting And Predecting Financial Statment Fraud : The Effectiveness of The Fraud Triangle SAS No 99. Advances in Financial Economics, Vol 13, Pages 53-81.
  • 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
  • Tran, M. (2002). WorldCom accounting scandal. Guardian. https://www.theguardian.com/business/2002/aug/09/corporatefraud.worldcom2
  • Tuanakotta, T. M. (2013). Mendeteksi manipulasi laporan keuangan. PT. Salemba Empat.
  • Widodo, A., Yusiana, R., & Anggi, S. (2017). How E-Marketing and Trust Influence Online Buying Decision : A Case Study of Matahari Mall.com in Bandung. Journal of Social Sciences & Humanities, 5(25), 107–114.
  • Zulzilawati, W. (2021). Beneish Ratio Index Sebagai Alat Deteksi Kecurangan. 12(2), 181–193. https://ejournal.uin-malang.ac.id/index.php/el-muhasaba/article/view/12803

PDF:

https://jurnal.harianregional.com/akuntansi/full-92753

Published

2023-01-26

How To Cite

PATMAWATI, Patmawati; RAHMAWATI, Meita. Deteksi Financial Statement Fraud : Model Beneish M-Score, dan Model F-Score.E-Jurnal Akuntansi, [S.l.], v. 33, n. 1, p. 34-44, jan. 2023. ISSN 2302-8556. Available at: https://jurnal.harianregional.com/akuntansi/id-92753. Date accessed: 08 Jul. 2024. doi:https://doi.org/10.24843/EJA.2023.v33.i01.p03.

Citation Format

ABNT, APA, BibTeX, CBE, EndNote - EndNote format (Macintosh & Windows), MLA, ProCite - RIS format (Macintosh & Windows), RefWorks, Reference Manager - RIS format (Windows only), Turabian

Issue

Vol 33 No 1 (2023)

Section

Articles

Creative Commons License This work is licensed under a Creative Commons Attribution 4.0 International License