Trigger Factors of Fraud Triangle Toward Fraud On Financial Reporting Moderated by Integration Of Technology Industry 4.0

  • Agoestina Mappadang Faculty of Economics, Universitas Budi Luhur, Indonesia
  • Yuliansyah Yuliansyah Faculty of Economics and Business, Universitas Lampung, Indonesia


This study examines triggers factors of the fraud triangle, core of all fraud auditing standards, for assessing the likelihood of fraudulent financial reporting. The Fourth Industrial Revolution (Industry 4.0) brings vital changes to industries, which forces people to face and act on these changes and may get impacted on fraudulent financial reporting. As Industry 4.0 ushers the use of new technology, the use of computerized systems for huge data analysis has advantage and disadvantage in audit and fraud detection. Therefore, our research uses the integration of technology 4.0 as a moderating variable on fraudulent financial reporting. This study also aims to determine fraud with the Beneish M-Score as a financial forensic tool to gage potential fraud in firms' financial statements. The population of this study was drawn from five priority sectors of the Making Indonesia 4.0 program, namely industries in five manufacturing subsectors listed on the Indonesia Stock Exchange. Results indicate that pressure does not have significant effect on fraudulent financial reporting. On the other hand, the opportunity with effective monitoring variable has a negative significant effect on fraudulent financial reporting, whereas rationalization has a positive significant effect on fraudulent financial reporting. The integration of Industry 4.0 variable moderates the effect of fraud on fraudulent financial reporting.

Keywords:  fraud triangle, technology industry, pressure, opportunity, rationalization


Download data is not yet available.


Acharya, V., V., & et al. (2011). Leverage, Moral Hazard, and Liquidity. Journal of Finance, 66(1).
Aprilia, & Sergius. (2015). The effectibeness of fraud triangle on detecting fraudulent financial statements : using Benesih model and the case of special companies. Riset Akuntansi Dan Keuangan, 3(3), 786–800.
Bambang, leo handoko. (2020). Big Data in Auditing for the Future of Data Driven Fraud Detection. International Journal of Innovative Technology and Exploring Engineering, 9(3).
Beneish, M. D. (1997). Detecting GAAP violation: Implications for assessing earnings management among firms with extreme financial performance. Journal of Accounting and Public Policy.
Beneish, M., Lee, C. M. C., & Nichols, D. C. (2012). Fraud Detection and Expected Returns. SSRN Electronic Journal.
Coram, P., Ferguson, C., & Moroney, R. (2008). Internal audit, alternative internal audit structures and the level of misappropriation of assets fraud. Accounting and Finance, 48, 543–559.
Coso, T. C. of S. O. of the T. C. (2013). Internal control - integrated framework: executive summary. In New York.
Cressey D.R. (1953). Other People’s money : a study in the psychology of embezzlement illinois. The Free Press.
Dalenogare, L. ., Benitez, G. ., & Ayala, N. . (2018). The expected contribution of Industry 4.0 technologies for industrial performance. International Journal Production Economic, 204, 383–394.
Darise, R. F. (2019). Detection of fraudulent financial statements using the Beneish ratio index for manufacturing companies listed on the Indonesia Stock Exchange in 2016 and 2017 period. Accountability, 8(2), 66–74.
Dechow, M. (1994). Accounting & Economics The role of accounting accruals. Journal of Accounting and Economics.
Donald, I., Taylor, P., Johnson, S., Cooper, C., Cartwright, S., & Robertson, S. (2005). Work environments, stress, and productivity: An examination using ASSET. International Journal of Stress Management, 12(4), 409–423.
Flemming R, & Baum. (1984). nd Unemployment StressBehavioral and Biochemical Effects of Job. Human Stress, 10(1), 12–17.
Germán, A., & Dalenogare, L. S. (2019). Industry 4.0 technologies: Implementation patterns in manufacturing companies. International Journal of Production Economics, 210, 15–26.
Ghozali, I. (2016). Ghozali, Imam. (2016). Aplikasi Analisis Multivariate dengan Program IBM SPSS 23. Semarang: BPFE Universitas Diponegoro. IOSR Journal of Economics and Finance.
Goel, S. (2014). The quality of reported numbers by the management. Journal of Financial Crime, 21(3), 355–376.
Hipgrave, S. (2013). Smarter fraud investigations with big data analytics. Network Security, 2013(12), 7–9.
Inayanti S, N., & Sukirman. (2016). The effect of factors in Fraud Diamond perspective on fraudulent financial reporting. Accounting, Analysis Journal, 5(3), 155–162.
Kagermann, H., Wahlster, W., & Helbig, J. (2013). Recommendations for Implementing the Strategic Initiative INDUSTRIE 4.0 - Final Report of the Industrie 4.0 Working Group. Industrie.
Kashmiri. (2014). Testing accruals based earnings management models in an international context.
Kim, H. ., Kotb, A., & Eldaly, M. . (2016). The use of generalized audit software by Egyptian external auditors. J. Appl. Account, 17(4), 456–478.
Korunka, C., Weiss, A., Huemer, K.-H., & Karetta, B. (1995). The Effect of New Technologies on Job Satisfaction and Psychosomatic Complaints. Applied Psychology, 14, 331–348.
KPMG. (2013). Fraud increase 38% to over 0.5bn but real cost is human misery. Retrieved from com/UK/en/IssuesAndInsights/ ArticlesPublications/Pages/fraud- barometer-2013.aspx.%0A %0A%0A
L, T., & Marfuah. (2015). Deteksi financial stetement fraud : pengujian dengan fraud diamond. Akuntansi Dan Auditing Indonesia, 19(2), 112–125.
Lou, Y.-I., & Wang, M.-L. (2011). Fraud Risk Factor Of The Fraud Triangle Assessing The Likelihood Of Fraudulent Financial Reporting. Journal of Business & Economics Research (JBER).
MacCarthy, J. (2017). Using Altman Z-score and Beneish M-score models to detect financial fraud and corporate failure : A case study of Enron Corporation. International Journal of Finance and Accounting, 6(6), 159–166.
Nugraheni, & Trihatmoko. (2018). Analisis faktor-faktor yang mempengaruhi terjadinya financial statement fraud : Perspektif Diamond Fraud Theory(Studi pada perusahaan perbankan yang terdaftar di Bursa Efek Indonesia periode 2014-2016No Title. Jurnal Akuntansi Dan Auditing, 14(2), 118–143.
Oktarigusta, & Lutfiana. (2017). Analisis fraud diamond untukmendeteksi terjadinya financial statement fraud di perusahaan (studi empiris pada perusahaan manufaktur yang terdaftar di BEI tahun 2012-2015). Ekonomi Manajemen Sumber Daya, 19(2), 92–108.
Oktaviani, E., & Karyawati, G. (2014). Factors Affecting Financial Statement Fraud : Fraud Triangle Approach. Economic & Business Research Festival Universitas Kristen Satya Wacana, 3, 1939–1955.
Rahayu, W. P., & Sopian, D. (2017). Pengaruh Rasio Keuangan Dan Ukuran Perusahaan Terhadap Financial Distress (Studi Empiris Pada Perusahaan Food and Beverage Di Bursa Efek Indonesia). Competitive Jurnal Akuntansi Dan Keuangan.
Rawson, G., Sarak, J., & Scott, D. (2016). How to scale personalized learning | McKinsey. In Mc Kinsey & Company.
Repousis, S. (2016). Using Beneish model to detect corporate financial statement fraud in Greece. Journal of Financial Crime, 23(4), 1063–1073.
Safig M, & Seles. (2018). The effect of external pressures, financial targets and financial distress on financiaol statement fraud. Advances in Economic , Business and Management Research Atlantic Press, 73, 57–61.
Singleton, T. W. (2010). Fraud Accounting & Forensic Accounting. New Jersey: John Willey & Sons.
Skousen, C. J., & Twedt, B. J. (2009). Fraud score analysis in emerging markets. Cross Cultural Management: An International Journal.
Sugiono. (2004). Konsep, identifikasi, alat analisis dan masalah penggunaan variabel moderator. Jurnal Studi Manajemen Dan Organisasi, 1(2), 61–70.
Sutton, S. (2014). Theory of planned behaviour. In Cambridge Handbook of Psychology, Health and Medicine, Second Edition.
Tao, F., & Cheng, J. (2018). Digital twin-driven product design, manufacturing and service with big data. International Journal Adv. Manufacturing Technologies, 94(9–12), 3563–3576.
Thoben, K. ., & Wiesner, S. (2017). Industrie 4.0 and smart manufacturing-a review of research issues and application examples. International Journal Autom. Technologies, 11(1).
Tuanakotta, T. M. (2010). Akuntansi Forensik & Audit Investigatif. In Edisi 4.
Ugochukwu, N., Emma, & Azuibuike. (2013). Beneish model as effective complement to the aplication of SAS no. 99 in the conduct of audit in Nigeria. Management and Administrative Sciences Review, 2(6), 640–655.
Zahro, & Yulia. (2018). Deteksi financial statement fraud dengan analisis fraud triangle pada perusahaan manufaktur. Riset Akuntansi, 7(9), 51–64.
Zanaria, Y. (2017). Pengaruh Aplikasi Teknologi, accounting reporting Terhadap Pencegahan Fraud dan serta imlplikasinya terhadap reaksi investor. Jurnal Akuisisi, 13(1), 91–100.
Zhong, & Zheng. (2017). Re-ranking person re-identification with k-reciprocal encoding. Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017.
Zimbelman, & Albrect et al. (2012). Forensic Accounting (4th editio). South Western: Cengange Learning.
How to Cite
MAPPADANG, Agoestina; YULIANSYAH, Yuliansyah. Trigger Factors of Fraud Triangle Toward Fraud On Financial Reporting Moderated by Integration Of Technology Industry 4.0. Jurnal Ilmiah Akuntansi dan Bisnis, [S.l.], v. 16, n. 1, p. 96-114, jan. 2021. ISSN 2303-1018. Available at: <>. Date accessed: 02 mar. 2021. doi: