Detecting Fraudulent Financial Reporting Using Artificial Neural Network
Abstract
This research aims to examine whether Artificial Neural Network (ANN) method can detect fraudulent financial reporting and whether firms are indicated to commit fraudulent financial reporting. The population in this research are firms listed on the Indonesia Stock Exchange in 2019 and companies that are confirmed to have committed fraudulent financial reporting. In total, 506 data sets were obtained through the purposive sampling technique. The data used in this research were obtained from financial statements. ANN method is used as the data analysis method in this research. Ten variables were used as fraud risk indicators to detect fraudulent financial reporting using ANN. Findings indicate that the developed ANN model can detect fraudulent financial reporting in financial statements. The findings of this research contribute to the literature on methods of detecting indications of financial statement fraud and that it can also be used to assist the auditor's role in detecting material misstatements attributable to fraud
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ACFE. 2020. “Report to The Nation on Occupational Fraud and Abuse Global Fraud Study”. Association of Certified Fraud Examiners, p. 1-80 Press.
ACFE. 2018. “Report to The Nation on Occupational Fraud and Abuse Global Fraud Study”. Association of Certified Fraud Examiners, p. 1-80 Press.
ACFE. 2016. “Report to The Nation on Occupational Fraud and Abuse Global Fraud Study”. Association of Certified Fraud Examiners, p. 1-80 Press.
ACFE. 2014. “Report to The Nation on Occupational Fraud and Abuse Global Fraud Study”. Association of Certified Fraud Examiners, p. 1-80 Press.
Cerullo, M. J., & Cerullo, V. (1999). Using neural networks to predict financial reporting fraud: Part 1. Computer Fraud and Security, 1999(5), 14–17. https://doi.org/10.1016/s1361-3723(99)80015-3
Dechow, P. M., Ge, W., Larson, C. R., & Sloan, R. G. (2011). Predicting Material Accounting Misstatements. Contemporary Accounting Research, 28(1), 17–82. https://doi.org/10.1111/j.1911-3846.2010.01041.x
Denziana, A. (2015). The effect of audit committee quality and internal auditor objectivity on the prevention of fraudulent financial reporting and the impact on financial reporting quality (a survey on state-owned company in Indonesia). International Journal of Monetary Economics and Finance, 8(2), 213–227. https://doi.org/10.1504/IJMEF.2015.070784
Hasnan, S., Mohd Razali, M. H., & Mohamed Hussain, A. R. (2020). The effect of corporate governance and firm-specific characteristics on the incidence of financial restatement. Journal of Financial Crime, 3. https://doi.org/10.1108/JFC-06-2020-0103
Kasmir, S.E, M. (2008). No Title (2008 (ed.)). Raja Grafindo Persada.
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. https://doi.org/10.1016/j.eswa.2006.02.016
Moeller, K. (2009). Intangible and financial performance: Causes and effects. Journal of Intellectual Capital, 10(2), 224–245. https://doi.org/10.1108/14691930910952632
Omar, N., Johari, Z. A., & Smith, M. (2017). Predicting fraudulent financial reporting using artificial neural network. Journal of Financial Crime, 24(2), 362–387. https://doi.org/10.1108/JFC-11-2015-0061
Purniati, A., & Heryana, T. (2018). Jurnal Aset (Akuntansi Riset). Jurnal ASET (Akuntansi Riset, 10(1), 63–74.
Rahma, D. V., & Suryani, E. (2019). No Title. JURNAL ASET (AKUNTANSI RISET), 11 (2)(Pengaruh Faktor-Faktor Fraud Triangle Terhadap Financial Statement Fraud).
Soeprajitno, R. R. W. N. (2019). Potensi Artificial Intelligence (Ai) Menerbitkan Opini Auditor ? Jurnal Riset Akuntansi Dan Bisnis Airlangga, 4(1), 560–573. https://doi.org/10.31093/jraba.v4i1.142
Yesiariani, M., & Rahayu, I. (2017). Deteksi financial statement fraud: Pengujian dengan fraud diamond. Jurnal Akuntansi & Auditing Indonesia, 21(1), 49–60. https://doi.org/10.20885/jaai.vol21.iss1.art5
ACFE. 2020. “Report to The Nation on Occupational Fraud and Abuse Global Fraud Study”. Association of Certified Fraud Examiners, p. 1-80 Press.
ACFE. 2018. “Report to The Nation on Occupational Fraud and Abuse Global Fraud Study”. Association of Certified Fraud Examiners, p. 1-80 Press.
ACFE. 2016. “Report to The Nation on Occupational Fraud and Abuse Global Fraud Study”. Association of Certified Fraud Examiners, p. 1-80 Press.
ACFE. 2014. “Report to The Nation on Occupational Fraud and Abuse Global Fraud Study”. Association of Certified Fraud Examiners, p. 1-80 Press.
Cerullo, M. J., & Cerullo, V. (1999). Using neural networks to predict financial reporting fraud: Part 1. Computer Fraud and Security, 1999(5), 14–17. https://doi.org/10.1016/s1361-3723(99)80015-3
Dechow, P. M., Ge, W., Larson, C. R., & Sloan, R. G. (2011). Predicting Material Accounting Misstatements. Contemporary Accounting Research, 28(1), 17–82. https://doi.org/10.1111/j.1911-3846.2010.01041.x
Denziana, A. (2015). The effect of audit committee quality and internal auditor objectivity on the prevention of fraudulent financial reporting and the impact on financial reporting quality (a survey on state-owned company in Indonesia). International Journal of Monetary Economics and Finance, 8(2), 213–227. https://doi.org/10.1504/IJMEF.2015.070784
Hasnan, S., Mohd Razali, M. H., & Mohamed Hussain, A. R. (2020). The effect of corporate governance and firm-specific characteristics on the incidence of financial restatement. Journal of Financial Crime, 3. https://doi.org/10.1108/JFC-06-2020-0103
Kasmir, S.E, M. (2008). No Title (2008 (ed.)). Raja Grafindo Persada.
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. https://doi.org/10.1016/j.eswa.2006.02.016
Moeller, K. (2009). Intangible and financial performance: Causes and effects. Journal of Intellectual Capital, 10(2), 224–245. https://doi.org/10.1108/14691930910952632
Omar, N., Johari, Z. A., & Smith, M. (2017). Predicting fraudulent financial reporting using artificial neural network. Journal of Financial Crime, 24(2), 362–387. https://doi.org/10.1108/JFC-11-2015-0061
Purniati, A., & Heryana, T. (2018). Jurnal Aset (Akuntansi Riset). Jurnal ASET (Akuntansi Riset, 10(1), 63–74.
Rahma, D. V., & Suryani, E. (2019). No Title. JURNAL ASET (AKUNTANSI RISET), 11 (2)(Pengaruh Faktor-Faktor Fraud Triangle Terhadap Financial Statement Fraud).
Soeprajitno, R. R. W. N. (2019). Potensi Artificial Intelligence (Ai) Menerbitkan Opini Auditor ? Jurnal Riset Akuntansi Dan Bisnis Airlangga, 4(1), 560–573. https://doi.org/10.31093/jraba.v4i1.142
Yesiariani, M., & Rahayu, I. (2017). Deteksi financial statement fraud: Pengujian dengan fraud diamond. Jurnal Akuntansi & Auditing Indonesia, 21(1), 49–60. https://doi.org/10.20885/jaai.vol21.iss1.art5
ACFE. 2020. “Report to The Nation on Occupational Fraud and Abuse Global Fraud Study”. Association of Certified Fraud Examiners, p. 1-80 Press.
ACFE. 2018. “Report to The Nation on Occupational Fraud and Abuse Global Fraud Study”. Association of Certified Fraud Examiners, p. 1-80 Press.
ACFE. 2016. “Report to The Nation on Occupational Fraud and Abuse Global Fraud Study”. Association of Certified Fraud Examiners, p. 1-80 Press.
ACFE. 2014. “Report to The Nation on Occupational Fraud and Abuse Global Fraud Study”. Association of Certified Fraud Examiners, p. 1-80 Press.
Cerullo, M. J., & Cerullo, V. (1999). Using neural networks to predict financial reporting fraud: Part 1. Computer Fraud and Security, 1999(5), 14–17. https://doi.org/10.1016/s1361-3723(99)80015-3
Dechow, P. M., Ge, W., Larson, C. R., & Sloan, R. G. (2011). Predicting Material Accounting Misstatements. Contemporary Accounting Research, 28(1), 17–82. https://doi.org/10.1111/j.1911-3846.2010.01041.x
Denziana, A. (2015). The effect of audit committee quality and internal auditor objectivity on the prevention of fraudulent financial reporting and the impact on financial reporting quality (a survey on state-owned company in Indonesia). International Journal of Monetary Economics and Finance, 8(2), 213–227. https://doi.org/10.1504/IJMEF.2015.070784
Hasnan, S., Mohd Razali, M. H., & Mohamed Hussain, A. R. (2020). The effect of corporate governance and firm-specific characteristics on the incidence of financial restatement. Journal of Financial Crime, 3. https://doi.org/10.1108/JFC-06-2020-0103
Kasmir, S.E, M. (2008). No Title (2008 (ed.)). Raja Grafindo Persada.
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. https://doi.org/10.1016/j.eswa.2006.02.016
Moeller, K. (2009). Intangible and financial performance: Causes and effects. Journal of Intellectual Capital, 10(2), 224–245. https://doi.org/10.1108/14691930910952632
Omar, N., Johari, Z. A., & Smith, M. (2017). Predicting fraudulent financial reporting using artificial neural network. Journal of Financial Crime, 24(2), 362–387. https://doi.org/10.1108/JFC-11-2015-0061
Purniati, A., & Heryana, T. (2018). Jurnal Aset (Akuntansi Riset). Jurnal ASET (Akuntansi Riset, 10(1), 63–74.
Rahma, D. V., & Suryani, E. (2019). No Title. JURNAL ASET (AKUNTANSI RISET), 11 (2)(Pengaruh Faktor-Faktor Fraud Triangle Terhadap Financial Statement Fraud).
Soeprajitno, R. R. W. N. (2019). Potensi Artificial Intelligence (Ai) Menerbitkan Opini Auditor ? Jurnal Riset Akuntansi Dan Bisnis Airlangga, 4(1), 560–573. https://doi.org/10.31093/jraba.v4i1.142
Yesiariani, M., & Rahayu, I. (2017). Deteksi financial statement fraud: Pengujian dengan fraud diamond. Jurnal Akuntansi & Auditing Indonesia, 21(1), 49–60. https://doi.org/10.20885/jaai.vol21.iss1.art5
DOI: https://doi.org/10.24198/jaab.v4i2.34914
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