DETECTING FRAUDS IN FINANCIAL STATEMENTS: A COMPREHENSIVE LITERATURE REVIEW BETWEEN 2019 AND 2023 (JUNE)

Year 2023, Volume: 18 Issue: 1, 47 - 51, 15.01.2024

Saadet Gaffaroglu Selçuk Alp

https://doi.org/10.17261/Pressacademia.2023.1849

Abstract

Purpose- The purpose of this study is to determine studies on detecting different types financial frauds, financial statement frauds and methods used in these studies. Financial statement fraud is one of the most common types of white-collar crime that has plagued various industries worldwide. It involves manipulating financial information in order to deceive stakeholders, such as investors and regulators, for personal gain or advantages. Financial statement fraud has significant implications for stakeholders, including investors, regulators, and the general public. Detecting fraudulent activities in financial statements is crucial for ensuring transparency, reliability, and trust in financial reporting.
Methodology- This paper presents a comprehensive literature review of studies focused on detecting frauds in financial statements in between 2019 and first half of 2023 inclusive on Science Direct.
Findings - The review encompasses a range of research articles, providing insights into various methodologies, techniques, and advancements in fraud detection. The findings of this review contribute to the understanding of fraud detection mechanisms in financial statements and inform future research directions in this critical area.
Conclusion - This paper presents a comprehensive literature review on the topic of detecting financial statement fraud, focusing on current trends and approaches employed in the field. By examining a wide range of scholarly articles, research studies, and industry reports, this review aims to provide an overview of the existing knowledge, methodologies, and tools utilized in the detection of financial statement fraud. In recent years, it has been observed that studies using machine learning in the field of fraud detection have increased.

Keywords

Fraud detection, machine learning, data mining literature review, financial statements

References

• ACFE. (2022). Report to the Nations on Occupational Fraud and Abuse. Retrieved from: http://www.acfe.com/rttn.aspx
• Al-Hashedi, K. G. and Magalingam, P. (2021). Financial fraud detection applying data mining techniques: A comprehensive review from 2009 to 2019. Computer Science Review, 40, 1-23.
• Bauer, T. D., Hillison, S. M., Peecher, M. E., & Pomeroy, B. (2020). Revising audit plans to address fraud risk: A case of “Do as I advise, not as I do”? Contemporary Accounting Research, 37(4), 2558-2589.
• Dimitrijevic, D., Jovkovic, B. and Milutinovic, S. (2021). The scope and limitations of external audit in detecting frauds in company’s operations. Journal of Financial Crime, 28(3), 632-646.
• Mongwe, W. T., Mbuvha, R. and Marwala, T. (2021). Bayesian inference of local government audit outcomes. Plos one, 16(12), e0261245.
• Roszkowska, P. (2021). Fintech in financial reporting and audit for fraud prevention and safeguarding equity investments. Journal of Accounting & Organizational Change, 17(2), 164-196.
• Simsek, S., Bayraktar, E., Ragothaman, S., and Dag, A. (2018, May). A Bayesian approach to detect the firms with material weakness in internal control. In 2018 Institute of Industrial and Systems Engineers Annual Conference and Expo, IISE 2018. Institute of Industrial and Systems Engineers (IISE).
• Thennakoon, A., Bhagyani, C., Premadasa, S., Mihiranga, S., and Kuruwitaarachchi, N. (2019, January). Real-time credit card fraud detection using machine learning. In 2019 9th International Conference on Cloud Computing, Data Science & Engineering (Confluence) (pp. 488-493). IEEE.
• Wang, S. (2010, May). A comprehensive survey of data mining-based accounting-fraud detection research. In 2010 International Conference on Intelligent Computation Technology and Automation (Vol. 1, pp. 50-53). IEEE.
• Xiaoyu, D., Jie, G., Shixuan, W. and Wei, S. (2018, July). A Literature Review on Financial Fraud. In 2018 15th International Conference on Service Systems and Service Management (ICSSSM) (pp. 1-4). IEEE.