Bambang Leo Handoko
ABSTRACT
The number of cases of fraud, especially fraud in the preparation and publication of financial statements is increasing. Both in the international world and also in Indonesia. The number of cases that have occurred for several years have only been uncovered, resulting in large losses for users of financial statements and stakeholders. The industrial revolution 4.0 opens up new opportunities, with the existence of big data analytics and forensic audits, which are expected to increase professional skepticism of auditors to be more observant in detecting fraudulent financial reports. Our research is a quantitative study, we tested the hypothesis between the independent variable and the moderating variable on the dependent variable. The independent variables in our research are professional skepticism, and big data analytics, then the moderating variable is forensic accounting, and the dependent variable is financial statement fraud detection. The object of our research is the auditor in a public accounting firm. We used the structural equation modeling partial least square as our data analysis. The results of our study state that professional skepticism, big data analytics have a significant impact on financial statement fraud detection. Forensic accounting moderate both professional skepticism and big data analytics.
- M. Meuldijk, “Impact of digitization on the audit profession,” KPMG AG Audit Comm. News, vol. 58, pp. 1–3, 2017, [Online]. Available: https://assets.kpmg.com/content/dam/kpmg/ch/pdf/ac-news-8-impact-digitization-en.pdf.Google Scholar
- J. Dai and M. A. Vasarhelyi, “Imagineering audit 4.0,” J. Emerg. Technol. Account., vol. 13, no. 1, pp. 1–15, 2016, doi: 10.2308/jeta-10494.Google ScholarCross Ref
- I. Pedrosa and C. Costa, “Computer assisted audit tools and techniques in real world: CAATT's applications and approaches in context,” Int. J. Comput. Inf. Syst. Ind. Manag. Appl., 2012.Google Scholar
- A. Rehman and F. Hashim, “Impact of Mature Corporate Governance on Detective Role of Forensic Accounting: Case of Public Listed Companies in Oman,” KnE Soc. Sci., vol. 2019, pp. 637–665, 2019, doi: 10.18502/kss.v3i22.5080.Google Scholar
- M. Lal Bhasin, “Corporate Accounting Fraud: A Case Study of Satyam Computers Limited,” Open J. Account., 2013, doi: 10.4236/ojacct.2013.22006.Google Scholar
- S. H. Tahir, M. Saleem, and H. Arshad, “Institutional Ownership and Corporate Value: Evidence From Karachi Stock Exchange ( KSE ) 30-Index Pakistan,” Prakt. menadžment, stručni časopis za Teor. i praksu menadžmenta, vol. 6, no. 1, pp. 41–49, 2015.Google Scholar
- B. L. Handoko and Natasya, “Fraud diamond model for fraudulent financial statement detection,” Int. J. Recent Technol. Eng., 2019, doi: 10.35940/ijrte.C5838.098319.Google Scholar
- A. Gepp, M. K. Linnenluecke, T. J. O'Neill, and T. Smith, “Big data techniques in auditing research and practice: Current trends and future opportunities,” J. Account. Lit., vol. 40, pp. 102–115, 2018, doi: 10.1016/j.acclit.2017.05.003.Google ScholarCross Ref
- G. L. Vousinas, “Advancing theory of fraud: the S.C.O.R.E. model,” J. Financ. Crime, vol. 26, no. 1, pp. 372–381, 2019, doi: 10.1108/JFC-12-2017-0128.Google ScholarCross Ref
- E. MacManus, “The audit of the future,” Account. Irel., 2017.Google Scholar
- C. E. Nwachukwu, T. O. Usman, S. O. Akhor, and A. O. Oladipupo, “Auditing in the New Age of Industry 4.0,” Int. J. Bus. Strateg. Autom., vol. 2, no. 1, pp. 17–28, 2020, doi: 10.4018/ijbsa.20210101.oa2.Google ScholarCross Ref
- D. Appelbaum, A. Kogan PhD, and M. A. Vasarhelyi PhD, “Introduction to Data Analysis for Auditors and Accountants: Certified Public Accountant,” CPA J., 2017.Google Scholar
- Azham, L. Teck Heang, R. Mohamad, and M. Ojo, “Internship and Audit Expectation Gap Among Undergraduate Students in Universiti Utara Malaysia,” J. Financ. Report. Account., vol. 6, no. 1, pp. 55–74, 2008, doi: 10.1108/19852510880000635.Google ScholarCross Ref
- R. Manita, N. Elommal, P. Baudier, and L. Hikkerova, “The digital transformation of external audit and its impact on corporate governance,” Technol. Forecast. Soc. Change, vol. 150, no. December 2018, p. 119751, 2020, doi: 10.1016/j.techfore.2019.119751.Google ScholarCross Ref
- B. Alao and O. L. Gbolagade, “An Assessment of How Industry 4.0 Technology is Transforming Audit Landscape and Business Models,” Int. J. Accounting, Financ. Manag. Res., vol. 3, no. 10, pp. 15–20, 2019, [Online]. Available: https://www.researchgate.net/publication/337021641.Google Scholar
- H. Guénin-Paracini, B. Malsch, and A. M. Paillé, “Fear and risk in the audit process,” Accounting, Organ. Soc., 2014, doi: 10.1016/j.aos.2014.02.001.Google Scholar
- A. Habib, “A meta analysis of the determinants of modified audit opinion decisions,” Manag. Audit. J., 2013, doi: 10.1108/02686901311304349.Google Scholar
- E. E. Griffith, J. S. Hammersley, K. Kadous, and D. Young, “Auditor mindsets and audits of complex estimates,” J. Account. Res., 2015, doi: 10.1111/1475-679X.12066.Google Scholar
- B. L. Handoko, R. L. Tjandra, L. A. A. Mozes, and G. M. Giok, “Model for Predicting Auditor Intention to Adopt Blockchain,” 2020.Google ScholarDigital Library
- M. D. Beneish and P. Vorst, “The Cost of Fraud Prediction Errors,” SSRN Electron. J., no. January, 2020, doi: 10.2139/ssrn.3529662.Google ScholarCross Ref
- A. Abbasi, C. Albrecht, A. Vance, and J. Hansen, “Metafraud: A meta-learning framework for detecting financial fraud,” MIS Q., vol. 36, no. 4, pp. 1293–1327, 2012.Google ScholarCross Ref
- O. Hakan, “An Analysis of Fraudulent Financial Reporting Using the Fraud Diamond Theory Perspective: An Empirical Study on the Manufacturing Sector Companies Listed on the Borsa Istanbul,” in Contemporary Issues in Audit Management and Forensic Accounting, vol. 102, S. Grima, E. Boztepe, and P. J. Baldacchino, Eds. Emerald Publishing Limited, 2020, pp. 131–153.Google Scholar
- W. S. Albrecht, C. Albrecht, and C. Albrecht, “Current Trends in Fraud and its Detection,” Inf. Secur. J. A Glob. Perspect., vol. 17, pp. 2–12, 2008, doi: 10.1080/19393550801934331.Google ScholarDigital Library
- J. T. Roscoe, “Fundamental research statistics for the behavioural sciences (2nd Edition),” Holt Rinehart & Winston, New York. 1975.Google Scholar
- D. Peltier-Rivest, “A model for preventing corruption,” J. Financ. Crime, vol. 25, no. 2, pp. 545–561, 2018, doi: 10.1108/JFC-11-2014-0048.Google ScholarCross Ref
- J. Barnes, “Fraud Detection: Forensic Accounting Education and CFE Designation Impact on Auditor's Confidence Levels,” J. Account. Financ., vol. 20, no. 4, 2020.Google Scholar
- A. Zarefar, Andreas, and A. Zarefar, “The Influence of Ethics, Experience and Competency toward the Quality of Auditing with Professional Auditor Scepticism as a Moderating Variable,” Procedia - Soc. Behav. Sci., 2016, doi: 10.1016/j.sbspro.2016.05.074.Google ScholarCross Ref
- I. Pedrosa and C. J. Costa, “New trends on CAATTs: What are the chartered accountants’ new challenges?,” 2014, doi: 10.1145/2618168.2618190.Google ScholarDigital Library
- I. G. N. A. P. Putra and G. S. Darma, “Is Bitcoin Accepted in Indonesia?,” Int. J. Innov. Sci. Res. Technol., vol. 4, no. 2, pp. 424–430, 2019.Google Scholar
- S. Patil, V. Nemade, and P. K. Soni, “Predictive Modelling for Credit Card Fraud Detection Using Data Analytics,” Procedia Comput. Sci., vol. 132, pp. 385–395, 2018, doi: 10.1016/j.procs.2018.05.199.Google ScholarDigital Library
- A. Armenakis and I. Lang, “Forensic Diagnosis and Transformation of an Organizational Culture,” J. Chang. Manag., 2014, doi: 10.1080/14697017.2013.843577.Google Scholar
- C. M. Lehmann, “Integrating Generalized Audit Software and Teaching Fraud Detection in Information Systems Auditing Courses,” J. Forensic Investig. Account., 2012.Google Scholar
Index Terms
- The Effect of Skepticism, Big Data Analytics to Financial Fraud Detection Moderated by Forensic Accounting
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