The Impact of Fraud on the Detection of Fraud in Financial Statements and Discretionary Accruals (Meta-Analysis Study)

Friska Dhea Narulita; Rahmawati Nur Baderi; Hwihanus Hwihanus

doi:10.47134/aaem.v1i4.348

Authors

Friska Dhea Narulita Faculty of Economics and Business, University of August 17, 1945 Surabaya
Rahmawati Nur Baderi Faculty of Economics and Business, University of August 17, 1945 Surabaya
Hwihanus Hwihanus Faculty of Economics and Business, University of August 17, 1945 Surabaya

DOI:

https://doi.org/10.47134/aaem.v1i4.348

Keywords:

Fraud Impact, Financial Statement Fraud Detection, Discretionary Accrual, Meta Analysis

Abstract

This article discusses the impact of fraud-on-fraud detection of financial statements and discretionary accruals through meta-analysis studies. Financial statement fraud is a serious problem that is increasingly prevalent in the global capital market, especially in developing countries such as China. This has driven the need for more sophisticated and effective detection methods. This study aims to identify factors that can affect the detection of financial statement fraud as well as the impact of fraud on the detection of financial statement fraud and discretionary accrual. This article also seeks to find better solutions in dealing with discretionary accruals that can affect investment decisions and public confidence in companies. The findings of the meta-analysis show that factors such as manipulation of accounting records, misrepresentation of information, and errors in the application of accounting principles can affect fraud detection. In addition, the impact of fraud on the detection of fraud in financial statements and discretionary accruals is also discussed comprehensively. This article suggests the need for more dynamic and responsive fraud detection methods to evolving risks.

References

Abdullahi, R. M. (2015). Fraud triangle theory and fraud diamond theory: Understanding the convergent and divergent for future research. European Journal of Business and Management , 7(28).

Aftabi, S. Z. (2023). Fraud detection in financial statements using data mining and GAN models. Expert Systems with Applications, 227. https://doi.org/10.1016/j.eswa.2023.120144

American Institute of Certifed Public Accountants (AICPA). (2019). Consideration of fraud in a financial statement audit.

Archna, R. (2024). Artificial intelligence challenges and its impact on detection and prevention of financial statement fraud: A theoretical study. Demystifying the Dark Side of AI in Business, 60–80. https://doi.org/10.4018/979-8-3693-0724-3.ch004

Ashtiani, M. N. (2022). Intelligent Fraud Detection in Financial Statements Using Machine Learning and Data Mining: A Systematic Literature Review. IEEE Access, 10, 72504–72525. https://doi.org/10.1109/ACCESS.2021.3096799

Ashtiani, M. N. (2023). An Efficient Resampling Technique for Financial Statements Fraud Detection: A Comparative Study. International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2023. https://doi.org/10.1109/ICECCME57830.2023.10253185

Cai, S. (2024). Explainable fraud detection of financial statement data driven by two-layer knowledge graph. Expert Systems with Applications, 246. https://doi.org/10.1016/j.eswa.2023.123126

Chen, X. (2023). Bagging or boosting? Empirical evidence from financial statement fraud detection. Accounting and Finance, 63(5), 5093–5142. https://doi.org/10.1111/acfi.13159

Gupta, S. (2024). Feature Selection for Dimension Reduction of Financial Data for Detection of Financial Statement Frauds in Context to Indian Companies. Global Business Review, 25(2), 323–348. https://doi.org/10.1177/0972150920928663

Hao Sun, J. L. (2023). Financial Fraud Detection Based On The Part-Of -Speech Feature Of Textual Risk Disclosure In Financial Report. Prosedia Computer Science, 57-64.

Huidong Wu, Y. C. (2022). Financial fraud risk analysis based on audit information knowledge graph. Procedia Computer Science 199, 780-787.

Okafor, K. J. (2023). Effect Of Opportunity And Rationalization On Financial Statement Fraud In Deposit Money Banks (DMBS). Journal Of Global Accounting, 54-69.

Oluwagbade, O. I. (2023). Fraud Diamond Model And Fraudulent Financial Reporting : Evidence From Deposit Money Banks In Nigeria. Journal Of Contemporary, 142-155.

Omeir, A. K. (2023). DETECTION OF FINANCIAL STATEMENTS FRAUD USING BENEISH AND DECHOW MODELS. Journal of Governance and Regulation, 12(3), 334–344. https://doi.org/10.22495/jgrv12i3siart15

Rahim, S. (2023). Auditors’ Experience in Financial Statement Fraud Detection: The Role of Professional Scepticism and Idealism. Management and Accounting Review, 22(3), 201–222.

R, K. a. (2015). "The model of fraud detection in financial statements by means of financial ratios,". Proc.-Social Behav. Sci, 213, 321-327. doi:10.1016/j.sbspro.2015.11.545

Shahana, T. (2023). State of the art in financial statement fraud detection: A systematic review. Technological Forecasting and Social Change, 192. https://doi.org/10.1016/j.techfore.2023.122527

Shengyong, W. X. (2022). An Analisys On FInancial Statement Fraud Detection For Chinese Listed Companies Using Deep Learning. IEEE Access, 22516-22532.

Shina, O. O. (2024). Fraud Pentagon Model And Discreationary Accruals In Deposit Money Banks In Nigeria. Journal Of Global Accounting, 95-118.

Soltani, M. (2023). Two decades of financial statement fraud detection literature review; combination of bibliometric analysis and topic modeling approach. Journal of Financial Crime, 30(5), 1367–1388. https://doi.org/10.1108/JFC-09-2022-0227

Wang, G. (2023). Attentive statement fraud detection: Distinguishing multimodal financial data with fine-grained attention. Decision Support Systems, 167. https://doi.org/10.1016/j.dss.2022.113913

Wasito, I. (2023). TIME SERIES CLASSIFICATION FOR FINANCIAL STATEMENT FRAUD DETECTION USING RECURRENT NEURAL NETWORKS BASED APPROACHES. Journal of Theoretical and Applied Information Technology, 101(23), 7972–7980.

Xiuguo, W. (2022). An Analysis on Financial Statement Fraud Detection for Chinese Listed Companies Using Deep Learning. IEEE Access, 10, 22516–22532. https://doi.org/10.1109/ACCESS.2022.3153478

Yadav, A. K. S. (2022). Unsupervised learning for financial statement fraud detection using manta ray foraging based convolutional neural network. Concurrency and Computation: Practice and Experience, 34(27). https://doi.org/10.1002/cpe.7340

Zhang, Y. (2022). Detection of fraud statement based on word vector: Evidence from financial companies in China. Finance Research Letters, 46. https://doi.org/10.1016/j.frl.2021.102477