Fraud Prediction from Financial Statements Using an Artificial Neural Network Model for Auditing
Article Sidebar
Main Article Content
Abstract
This research aims to develop an Artificial Neural Network (ANN) model for detecting financial fraud trends from the financial statements of companies listed on the Stock Exchange of Thailand. By reviewing literature and concepts related to agency theory, which is associated with the fraud triangle concept and the presentation of financial statements, to detect fraud using the traditional Beneish M-Score, and the advancement of artificial neural network technology that matches the limitations of auditing information stored in database format, enabling auditors to use technology in auditing. Therefore, this research employs an artificial neural network model to predict fraud quickly and accurately from financial statements as a primary tool for analyzing and predicting fraud trends. The results from evaluating the performance of the developed model show high efficiency, with the best artificial neural network model achieving an overall accuracy of 94.03%, and after calibration, the model has a confidence level of 80.41%. The analysis of Feature Importance that has a significant influence on prediction is the Current Ratio. These results indicate that the developed artificial neural network model is a valuable tool for planning and improving the auditing process that can accurately and reliably predict fraud in financial statements, and it may. further develop the artificial neural network model into an additional internal auditing tool
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
The content within the published articles, including images and tables, is copyrighted by Rajamangala University of Technology Rattanakosin. Any use of the article's content, text, ideas, images, or tables for commercial purposes in various formats requires permission from the journal's editorial board.
Rajamangala University of Technology Rattanakosin permits the use and dissemination of article files under the condition that proper attribution to the journal is provided and the content is not used for commercial purposes.
The opinions and views expressed in the articles are solely those of the respective authors and are not associated with Rajamangala University of Technology Rattanakosin or other faculty members in the university. The authors bear full responsibility for the content of their articles, including any errors, and are responsible for the content and editorial review. The editorial board is not responsible for the content or views expressed in the articles.
References
กรมบังคับคดี. (2566). DSI แถลงความคืบหน้ากรณีหุ้น STARK (คดีพิเศษที่ 57/2566). https://www.dsi.go.th/th/Detail/e34fb66360cc0cc2186 a1951f5aab66f
ผัลย์ศุภา หลงทอง และ นิ่มนวล วิเศษสรรพ์. (2562). ความสัมพันธ์ระหว่างความเสี่ยงที่เป็นระบบและอัตราส่วนทางการเงิน กรณีบริษัทจดทะเบียนในตลาดหลักทรัพย์แห่งประเทศไทย SET 100. นำเสนอในงาน The Rangsit Graduate Research Conference: RGRC, กรุงเทพมหานคร.
พรรณนิภา รอดวรรณะ. (2564). ระบบ Forensic Analytics ขั้นพื้นฐานในการตรวจสอบการทุจริตของงบการเงิน: กรณีศึกษาบริษัทในตลาดหลักทรัพย์แห่งประเทศไทย. วารสารสภาวิชาชีพบัญชี, 3(8), 4-21.
ศิลปพร ศรีจั่นเพชร. (2551). ทฤษฎีบรรษัทภิบาล. วารสารบริหารธุรกิจ มหาวิทยาลัยธรรมศาสตร์, 31(120), 1-4.
สภาวิชาชีพบัญชี ในพระบรมราชูปถัมภ์. (2555ก). ความรับผิดชอบของผู้สอบบัญชีเกี่ยวกับการพิจารณาการทุจริตในการตรวจสอบงบการเงิน [มาตรฐานการสอบบัญชี รหัส 240]. https://acpro-std.tfac.or.th/test_std/uploads/files/ TSA/2564_TSA240.pdf
สภาวิชาชีพบัญชี ในพระบรมราชูปถัมภ์. (2555ข). วัตถุประสงค์โดยรวมของผู้สอบบัญชีรับอนุญาตและการปฏิบัติงานตรวจสอบตามมาตรฐานการสอบบัญชี [มาตรฐานการสอบบัญชี รหัส 200]. https://acpro-std.tfac.or.th/test_std/uploads/files/TSA/2564_TSA200.pdf
สภาวิชาชีพบัญชี ในพระบรมราชูปถัมภ์. (2559). การแสดงความเห็นและการรายงานต่องบการเงิน [มาตรฐานการสอบบัญชี รหัส 700 (ปรับปรุง)].https://acpro-std.tfac.or.th/test_std/ uploads/files/TSA/2564_TSA700(Revised).pdf
สภาวิชาชีพบัญชี ในพระบรมราชูปถัมภ์. (2563). กรอบแนวคิดสำหรับการรายงานทางการเงิน [ประกาศสภาวิชาชีพบัญชี ที่ 48/2563] https://eservice.tfac.or.th/get_file/index.php? file=Frame%20work_03.10.66.pdf
ACFE. (2020). Report to the nations: 2020 global study on occupational fraud and abuse. https://www.acfe.com/report-to-the-nations/2020/
ACFE. (2021). Fraud triangle. https://www.acfe.com/ fraud-triangle.aspx%C2%A0
Anderson, D., & McNeill, G. (1992). Artificial neural networks technology. https://www.academia.edu/download/33786328/Artificial_ Neural_Networks _Technology.pdf
Bao, W., Yue, J., & Rao, Y. (2017). A deep learning framework for financial time series using stacked autoencoders and long-short term
memory. PloS one, 12(7), e0180944. https://doi.org/10.1371/journal.pone.0180944
Beneish, M.D. (1999). The detection of earnings manipulation. Financial Analysts Journal, 55(5), 24-36.
Beneish, M.D., & Vorst, P. (2020). The cost of fraud prediction errors. SSRN 3529662.
Caouette, J. B., Altman, E. I., Narayanan, P., & Altman, Z. (1968). 1.6 Z-score estimated for non-manufacturers & emerging markets. Altman Z-Score, 189(1).
Dashtbayaz, M.L., & Mohammad, S. (2015). Data search and discovery process for financial statement fraud. Research Journal of Finance and Accounting, 6(3), 75-80.
Healy, P.M., & Palepu, K.G. (2003). The fall of Enron. Journal of Economic Perspectives, 17(2), 3-26.
https://doi.org/10.1257/089533003765888403
Hribar, P., Kravet, T., & Wilson, R. (2014). A new measure of accounting quality. Review of Accounting Studies, 19(1), 506-538. https://doi.org/10.1007/s11142-013-9253-8
Japkowicz, N. (2001). Supervised versus unsupervised binary-learning by feedforward neural networks. Machine Learning, 42, 97-122.
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.
Jiang, W. (2021). Applications of deep learning in stock market prediction: Recent progress. Expert Systems with Applications, 184, 115537. https://doi.org/10.1016/j.eswa.2021.115537
Kak, S. (1993). On training feedforward neural networks. Pramana, 40(1), 35-42. https://doi.org/10.1007/BF02898040
Kim, S., & Upneja, A. (2014). Predicting restaurant financial distress using decision tree and AdaBoosted decision tree models. Economic Modelling, 36, 354-362. https://doi.org/10.1016/j.econmod.2013.10.005
Kusumawati, A., & Syamsuddin, S. (2018). The effect of auditor quality to professional skepticsm and its relationship to audit quality. International Journal of Law and Management, 60(4), 998-1008. https://doi.org/10.1108/IJLMA-03-2017-0062
Lechner, M. (2020). Learning representations for binary-classification without backpropagation. In 8th International Conference on Learning Representations (pp 1-17). Virtual; Addis Ababa, Ethiopia: ICLR.
Mubako, G., & O'Donnell, E. (2018). Effect of fraud risk assessments on auditor skepticism: Unintended consequences on evidence evaluation. International Journal of Auditing, 22(1), 55-64. https://doi.org/10.1111/ijau.12104
Nawaiseh, A.K., Abbod, M.F., & Itagaki, T. (2020). Financial statement audit using support vector machines, artificial neural networks and K-nearest neighbor: An empirical study of UK and Ireland. International Journal of Simulation Systems, Science & Technology, 21(2), 1-6. https://doi.org/10.5013/IJSSST.a.21.02.07
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
Pricewaterhousecoopers. (2020). Staying on top of a never-ending war. https://www.pwc.com/th/en/consulting/forensic/economic-crime-and-fraud-in-thailand.html
Rahim, S., Muslim, M., & Amin, A. (2019). Red flag and auditor experience toward criminal detection through professional skepticism. Jurnal Akuntans, 23(1), 47-62. https://doi.org/10.24912/ja.v23i1.459
Suryandari, N., & Yuesti, A. (2017). Professional scepticism and auditors ability to detect fraud based on workload and characteristics of auditors. Scientific Research Journal (SCIRJ), 5(9), 109-115.
Topor, D.I. (2017). The auditor's responsibility for finding errors and fraud from financial situations: Case study. International Journal of Academic Research in Accounting, Finance and Management Sciences, 7(1), 342-352. https://doi.org/10.6007/IJARAFMS/v7-i1/2862
Turner, J.L., Mock, T.J., & Srivastava, R.P. (2003). An analysis of the fraud triangle. The University of Memphis, University of Southern California, University of Kansas.
Yazid, H., & Suryanto, T. (2017). IFRS, professional auditor skepticism, conflict agency to prevention of fraud and investor confidence level. International Journal of Economic Perspectives, 11(1), 250-259.