scholarly journals ANALISIS BENEISH M-SCORE UNTUK MENDETEKSI FINANCIAL STATEMENT FRAUD PADA PT. GARUDA INDONESIA Tbk PERIODE 2017-2019

2021 ◽  
Vol 6 (1) ◽  
pp. 44-51
Author(s):  
Dedi Julianto ◽  
Marjono Marjono ◽  
Aminullah Labangge

This study aims to detect financial statement fraud at PT. Garuda Indonesia in 2017-2019 used the Beneish M Score model and testing whether the Beneish M Score model can detect the occurrence of financial statement fraud at PT. Garuda Indonesia in 2018. The results of this study indicate that PT Garuda Indonesia was classified in the Gray criteria, that can be interpreted as manipulate or not manipule financial statements in 2017 and 2018 and classified in the criteria not manipulate financial statements in 2019. The results of this study also found that the Beneish M Score model could not accurately to detect the manipulation of financial statements at PT. Garuda Indonesia in 2018, because only two ratios are Gross Margin Index (GMI) and Days Sales in Receivable Index (DSRI) which detect manipulation of financial statements, so they are only classified in the gray area criteria

2020 ◽  
Vol 25 (1) ◽  
pp. 29-44
Author(s):  
Mariati ◽  
Emmy Indrayani

Company’s financial condition reflected in the financial statements. However, there are many loopholes in the financial statements which can become a chance for the management and certain parties to commit fraud on the financial statements. This study aims to detect financial statement fraud as measured using fraud score model that occurred in issuers entered into the LQ-45 index in 2014-2016 with the use of six independent variables are financial stability, external pressure, financial target, nature of industry, ineffective monitoring and rationalization. This study using 27 emiten of LQ-45 index during 2014-2016. However, there are some data outlier that shall be removed, thus sample results obtained 66 data from 25 companies. Multiple linear regression analysis were used in this study. The results showed that the financial stability variables (SATA), nature of industry (RECEIVBLE), ineffective monitoring (IND) and rationalization (ITRENDLB) proved to be influential or have the capability to detect financial statement fraud. While the external pressure variables (DER) and financial target (ROA) are not able to detect the existence of financial statement fraud. Simultaneously all variables in this study were able to detect significantly financial statement fraud.


2021 ◽  
Author(s):  
Ponny Harsanti ◽  
Ulva Rizky Mulyani

Fraudulent financial statements are disclosures of a company’s financial condition which are intentionally made inaccurate by eliminating values. This study aimed to examine the effect of using the Beneish M-Score model against fraud reports of manufacturing companies listed in the Indonesian Stock Exchange in 2016 -2018. Purposive sampling was used and the sample consisted of 69 companies. The results showed that the Days Sales in Receivables Index, Gross Margin Index and Total Accrual to Total Asset had a significant positive effect on financial statement fraud, while the Asset Quality Index, Sales Growth Index, Depreciation Index, Sales General and Administrative Expenses Index, and Leverage Index did not affect fraudulent financial statements. Keywords: fraud, Beneish, logistic regression


2016 ◽  
Vol 23 (4) ◽  
pp. 1063-1073 ◽  
Author(s):  
Spyridon Repousis

Purpose This paper aims to investigate empirically the eight-variables Beneish M-model to identify occurrence of financial statement fraud or tendency to engage in earning manipulation. Design/methodology/approach A data set of 25,468 companies (Société Anonyme and Limited Liability Companies) in Greece was analyzed during two-year period of 2011-2012. Financial statements of banks are excluded. Findings The results showed that 8,486 companies or 33 per cent of the whole sample has a greater than −2.2 score, which is a signal that companies are likely to be manipulators. Also, for manipulators, results using F-distribution showed that days sales in receivable index (DSRI), asset quality index (AQI), depreciation index, selling, general and administrative expenses index (SGAI), total accruals to total assets index and leverage index (LVGI) are significant at 99 per cent confidence level in its effect on Beneish M-score. Also, there is a significant relationship between earning management, as expressed by Beneish M-score and each one of variables, DSRI, AQI, gross margin index, sales growth index, SGAI and LVGI. Most of all, DSRI explains 95.92 per cent of the variation in Beneish M-score in statistical terms. Practical implications Results are important for banking system, because financial statements information influence credit decisions of banks. Debt agreements include terms based upon accounting numbers. Also, using Beneish Model, it is a cheap and easy way for examiners of possible fraudulent activity. Originality/value To the best of the author’s knowledge, there is a great lack of research in Greece, using Beneish model. There is only one more study using the Beneish model, examining only a few companies listed in Athens Stock Exchange during 1999-2000. Findings have also important implications not only for banks but also for users of Greek financial statement accounts, especially to investors, auditors, regulators, to taxation and other state authorities.


2020 ◽  
Vol 4 (1) ◽  
pp. 32-41
Author(s):  
Muhammad Bagus ◽  
Noviansyah Rizal ◽  
Siwidyah Desi Lastianti

This study aims to determine the Pentagon Determinant Fraud in detecting fraudulent financial statements. Fraudulent financial statements are proxied by the Fraud Score Model. Whereas the pressure factor is proxied by insisting from within, for the opportunity factor proxied by industry conditions, the rationalization factor is proxied by the ratio of total accruals, the competency factor is proxied by the change of directors and arrogance is proxied by the duality of quality positions at the CEO. The population in this study amounted to 100 companies incorporated in the compass index 100 contained in the Indonesia Stock Exchange and for the sample of the study were 35 companies belonging to the compass index 100 contained in the Indonesia Stock Exchange, which was selected using the purposive sampling method for the 2017-2018 period. Data were analyzed using multiple linear regression. Based on the test results, it was concluded that the pentagon fraud component included internal pressure (LEV), industry conditions (INVENTORY), rationalization (TATA) influencing financial statement fraud while competence (DCHANGE) and arrogance (DCD) had no effect on financial fraud statement. This proves that internal pressure (LEV), industry conditions (INVENTORY), and rationalization (TATA) can be used to detect fraud in financial statements.


2020 ◽  
Vol 7 (1) ◽  
pp. 7
Author(s):  
Fitri Aulia Rachmi ◽  
Djoko Supatmoko ◽  
Bunga Maharani

Penelitian ini bertujuan menguji dan menganalisis penggunaan model Beneish M-Score untuk mendeteksi financial statement fraud. Data yang digunakan adalah data sekunder berupa laporan keuangan perusahaan pertambangan terbuka di Indonesia. Metode penelitian yang digunakan adalah metode kuantitatif dengan analisis diskriminan. Metode analisis diskriminan digunakan untuk menganalisa hubungan antara model Beneish M-Score dengan financial statement fraud dengan cara melihat faktor atau variabel mana yang secara nyata dapat mempengaruhi variabel dependen. Pengaplikasian analisis diskriminan dilakukan untuk menguji variabel independen manakah yang secara akurat dapat membedakan sampel laporan keuangan yang diduga telah dimanipulasi dan laporan keuangan yang diduga tidak dimanipulasi. Hasil penelitian menunjukkan bahwa variabel yang mampu membedakan sampel laporan keuangan yang diduga telah dimanipulasi dan diduga tidak dimanipulasi adalah variabel Days Sales Receivable Index (DSRI), Gross Margin Index (GMI), Sales Growth Index (SGI), dan Total Accrual to Total Asset (TATA) sementara variabel Asset Quality Index (AQI), Depreciation Index (DEPI), Sales and General Administration Expenses Index (SGAI), Leverage Index (LVGI) terbukti tidak mampu membedakan laporan keuangan yang diduga telah dimanipulasi dan diduga tidak dimanipulasi.


Jurnalku ◽  
2021 ◽  
Vol 1 (4) ◽  
pp. 278-286
Author(s):  
Kodirin

Penelitian ini bertujuan untuk mengetahui apakah laporan keuangan PT Rekayasa Industri (Rekind) tahun 2018 terindikasi dimanipulasi atau tidak.  Penelitian ini bersifat deskriptif dengan menggunakan data sekunder. Laporan keuangan komparatif Rekind tahun 2018 yang tersedia di website Rekind menjadi objek penelitian ini.  Rekind mengalami kesulitan keuangan akibat perselisihan dengan PT Amara Panca Utama terkait proyek Banggai Ammonia Plant (BAP).  Model Beneish M-Score digunakan untuk mengukur potensi manipulasi pada laporan keuangan. Dugaan penelitian ini adalah PT Rekayasa Industri terindikasi memanipulasi laporan keuangan tahun 2018 mengingat kesulitan keuangan yang dialami. Hasil analisis menyimpulkan bahwa berdasarkan model Beneish M-Score laporan keuangan Rekind tahun 2018 terindikasi telah dimanipulasi.  Kesimpulan ini diambil berdasarkan nilai Beneish M-Score laporan keuangan Rekind tahun 2018 sebesar -1,49, lebih besar daripada nilai patokan, -2,22.  Temuan ini sejalan dengan temuan Y. A. Nugroho (2017) bahwa perusahaan melakukan manipulasi laporan keuangan sebagai salah satu cara untuk memperbaiki kondisi perusahaan yang buruk akibat mengalami kesulitan keuangan dan Haq et al (2017) bahwa financial distress berpengaruh negatif terhadap integritas laporan keuangan.  Terdapat dua rasio yang mengindikasikan telah dimanipulasi yaitu GMI dan TATA; tiga  rasio yang mengindikasikan tidak dimanipulasi yaitu  DSRI, AQI, dan SGAI; dan tiga rasio yang berada pada “grey area” yaitu SGI, DEPI, dan LVGI.   This study aims to test whether there is an indication of manipulation in the financial statements of PT Rekayasa Industri (Rekind) in 2018 or not. This research is descriptive using secondary data. The object of this research is the 2018 Rekind comparative financial Report available on the Record website. Rekind has experienced financial difficulties due to disputes with PT Amara Panca Utama regarding the Project of the Banggai Ammonia Plant. The method used to measure manipulation potential is the Beneish M-Score model. Rekind allegedly conducted manipulation to "improve" financial statements in 2018 as a way out of financial difficulties that hit. The results of the analysis concluded that the account was indicated to carry out manipulation for the financial statements of 2018. This conclusion was taken based on the value of the Beneish M-score of the 2018 financial statement of -1.49, greater than the benchmark value, -222. This finding is in line with the research before which concludes that the company conducts financial statements manipulation as one way to cover the condition of a bad company due to experiencing financial difficulties and that financial distress influencing the integrity of financial statements negatively. There are two indexes that are indicated to be manipulated, namely GMI and TATA; three indexes indicated that are not manipulated, namely DSRI, AQI, and SGAI; and three indexes that are on gray area, SGI, DEPI, and LVGI.


Author(s):  
Nguyen Tien Hung ◽  
Huynh Van Sau

The study was conducted to identify fraudulent financial statements at listed companies (DNNY) on the Ho Chi Minh City Stock Exchange (HOSE) through the Triangular Fraud Platform This is a test of VSA 240. At the same time, the conformity assessment of this model in the Vietnamese market. The results show that the model is based on two factors: the ratio of sales to total assets and return on assets; an Opportunity Factor (Education Level); and two factors Attitude (change of independent auditors and opinion of independent auditors). This model is capable of accurately forecasting more than 78% of surveyed sample businesses and nearly 72% forecasts for non-research firms.  Keywords Triangle fraud, financial fraud report, VSA 240 References Nguyễn Tiến Hùng & Võ Hồng Đức (2017), “Nhận diện gian lận báo cáo tài chính: Bằng chứng thực nghiệm tại các doanh nghiệp niêm yết ở Việt Nam”, Tạp chí Công Nghệ Ngân Hàng, số 132 (5), tr. 58-72.[2]. Hà Thị Thúy Vân (2016), “Thủ thuật gian lận trong lập báo cáo tài chính của các công ty niêm yết”, Tạp chí tài chính, kỳ 1, tháng 4/2016 (630). [3]. Cressey, D. R. (1953). Other people's money; a study of the social psychology of embezzlement. New York, NY, US: Free Press.[4]. Bộ Tài Chính Việt Nam, (2012). Chuẩn mực kiểm toán Việt Nam số 240 – Trách nhiệm của kiểm toán viên đối với gian lận trong kiểm toán báo cáo tài chính. [5]. 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.[6]. Võ Hồng Đức & Phan Bùi Gia Thủy (2014), Quản trị công ty: Lý thuyết và cơ chế kiểm soát, Ấn bản lần 1, Tp.HCM, Nxb Thanh Niên.[7]. Freeman, R. E. (1984). Strategic management: A stakeholder approach. Boston: Pitman independence on corporate fraud. Managerial Finance 26 (11): 55-67.[9]. 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. Available at SSRN 1295494.[10]. Lou, Y. I., & Wang, M. L. (2011). Fraud risk factor of the fraud triangle assessing the likelihood of fraudulent financial reporting. Journal of Business and Economics Research (JBER), 7(2).[11]. Perols, J. L., & Lougee, B. A. (2011). The relation between earnings management and financial statement fraud. Advances in Accounting, 27(1), 39-53.[12]. Trần Thị Giang Tân, Nguyễn Trí Tri, Đinh Ngọc Tú, Hoàng Trọng Hiệp và Nguyễn Đinh Hoàng Uyên (2014), “Đánh giá rủi ro gian lận báo cáo tài chính của các công ty niêm yết tại Việt Nam”, Tạp chí Phát triển kinh tế, số 26 (1) tr.74-94.[13]. Kirkos, E., Spathis, C., & Manolopoulos, Y. (2007). Data mining techniques for the detection of fraudulent financial statements. Expert Systems with Applications, 32(4), 995-1003.[14]. Amara, I., Amar, A. B., & Jarboui, A. (2013). Detection of Fraud in Financial Statements: French Companies as a Case Study. International Journal of Academic Research in Accounting, Finance and Management Sciences, 3(3), 40-51.[15]. Beasley, M. S. (1996). An empirical analysis of the relation between the board of director composition and financial statement fraud. Accounting Review, 443-465.[16]. Beneish, M. D. (1999). The detection of earnings manipulation. Financial Analysts Journal, 55(5), 24-36.[17]. Persons, O. S. (1995). Using financial statement data to identify factors associated with fraudulent financial reporting. Journal of Applied Business Research (JABR), 11(3), 38-46.[18]. Summers, S. L., & Sweeney, J. T. (1998). Fraudulently misstated financial statements and insider trading: An empirical analysis. Accounting Review, 131-146.[19]. Dechow, P. M., Sloan, R. G., & Sweeney, A. P. (1996). Causes and consequences of earnings manipulation: An analysis of firms subject to enforcement actions by the SEC. Contemporary accounting research, 13(1), 1-36.[20]. Loebbecke, J. K., Eining, M. M., & Willingham, J. J. (1989). Auditors experience with material irregularities – Frequency, nature, and detectability. Auditing – A journal of practice and Theory, 9(1), 1-28. [21]. Abbott, L. J., Park, Y., & Parker, S. (2000). The effects of audit committee activity and independence on corporate fraud. Managerial Finance, 26(11), 55-68.[22]. Farber, D. B. (2005). Restoring trust after fraud: Does corporate governance matter?. The Accounting Review, 80(2), 539-561.[23]. Stice, J. D. (1991). Using financial and market information to identify pre-engagement factors associated with lawsuits against auditors. Accounting Review, 516-533.[24]. Beasley, M. S., Carcello, J. V., & Hermanson, D. R. (1999). COSO's new fraud study: What it means for CPAs. Journal of Accountancy, 187(5), 12.[25]. Neter, J., Wasserman, W., & Kutner, M. H. (1990). Applied statistical models.Richard D. Irwin, Inc., Burr Ridge, IL.[26]. Gujarati, D. N. (2009). Basic econometrics. Tata McGraw-Hill Education.[27]. McFadden, D. (1974). Conditional Logit Analysis of Qualita-tive Choice Behavior," in Frontiers in Econometrics, P. Zarenm-bka, ed. New York: Academic Press, 105-42.(1989). A Method of Simulated Moments for Estimation of Discrete Response Models Without Numerical Integration," Econometrica, 54(3), 1027-1058.[28]. DA Cohen, ADey, TZ Lys. (2008), “Accrual-Based Earnings Management in the Pre-and Post-Sarbanes-Oxley Periods”. The accounting review.


2019 ◽  
Vol 4 (2) ◽  
pp. 128-138
Author(s):  
Faiz Rahman Siddiq ◽  
Agus Endrianto Suseno

Financial statement fraud biasa disebut dengan kecurangan laporan keuangan yang merupakan kesengajaan dalam melakukan kelalaian dan kesalahan ketika  membuat laporan keuangan dengan penyajian yang tidak sesuai pada prinsip akuntansi berterima umum. Statement on Auditing Standards (SAS) No.99 menjelaskan tentang salah saji yang berhubungan dengan auditor dalam mengaudit laporan keuangan terhadap fraud diantaranya adalah (1) salah saji dari kesalahan suatu laporan keuangan merupakan suatu  pengungkapan yang direncanakan guna menipu pengguna laporan keuangan, (2) penyalahgunaan aset atau istilah lain pencurian dan penggelapan sering dijadikan sebagai salah saji dalam laporan keuangan. Fraud pentagon theory merupakan pengembangan dari teori fraud sebelumnya yaitu fraud triangle (Cressey, 1953) dan fraud diamond (Wolf and Hermanson,2004). Populasi penelitian ini adalah perusahaan yang tergabung dalam Indeks JII (Jakarta Islamic Index) pada tahun 2014-2017. Teknik pengambilan sampel dengan menggunakan metode purposive sampling. Metode analisis data yang digunakan dalam penelitian ini adalah analisis regresi linear berganda. Financial statement fraud dalam penelitian menggunakan perspektif F-Score Model. Hasil penelitian ini adalah pressure (Financial Stability, dan Financial Target), dan Opportunity (Nature of Industry) berpengaruh terhadap financial statement fraud. Sedangkan Pressure (External Pressure dan Personal Financial  Need), Rationalization (Change in Auditor), Competence (Change of Director) dan Arrogance (Frequent Number of CEO’s Picture dan Dualism Position) tidak berpengaruh terhadap financial statemnt fraud.


2021 ◽  
Vol 22 (11) ◽  
pp. 1262-1275
Author(s):  
Sergei V. ARZHENOVSKII ◽  
Tat'yana G. SINYAVSKAYA ◽  
Andrei V. BAKHTEEV

Subject. This article assesses the propensity for material misstatement risk due to unfair actions of persons charged with the financial statements preparation, based on their behavioral traits. Objectives. The article aims to develop a scoring type methodology for identifying the propensity for material misstatement risk due to unfair actions of persons charged with the financial statements preparation. Methods. For the study, we used a multidimensional statistical method of discriminant analysis based on empirical data from an author-conducted survey of 515 employees charged with the financial statements preparation in companies. Results. The article presents a two-stage methodology that helps estimate whether a person has traits associated with a hyperpropensity for financial statements fraud risk. Conclusions and Relevance. The developed methodology for detecting the fraud risk is easy to use. It gives the result in binary form and does not violate the principles of audit ethics. The estimated material misstatement risk due to unfair actions makes it possible to justify the need for appropriate audit procedures when developing a strategy and audit plan.


2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Paulina Roszkowska

Purpose The purpose of this paper is to explore the audit-related causes of financial scandals and advice on how emerging technologies can provide solutions thereto. Specifically, this study seeks to look at the facilitators of financial statement fraud and explain specific fintech advancements that contribute to financial information reliability for equity investments. Design/methodology/approach The study uses the case studies of Enron and Arthur Andersen to document the evidence of audit-related issues in historical financial scandals. Then, a comprehensive and interdisciplinary literature review at the intersection of business, accounting and engineering, provides a foundation to propose technology advancements that can solve identified problems in accounting and auditing. Findings The findings show that blockchain, internet of things, smart contracts and artificial intelligence solutions have different functionality and can effectively solve various financial reporting and audit-related problems. Jointly, they have a strong potential to enhance the reliability of the information in financial statements and generally change how companies operate. Practical implications The proposed and explained technology advancements should be of interest to all publicly listed companies and investors, as they can help safeguard equity investments, thus build investors’ trust towards the company. Social implications Aside from implications for capital markets participants, the study findings can materially benefit various stakeholder groups, the broader company environment and the economy. Originality/value This is the first paper that seeks solutions to financial fraud and audit-related financial scandals in technology and not in implementing yet another regulation. Given the recent technology advancements, the study findings provide insights into how the role of an external auditor might evolve in the future.


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