International Journal of Finance & Managerial Accounting

International Journal of Finance & Managerial Accounting

Identifying Factors Affecting on Managers' Fraudulent Reporting and Providing a Model for its Prediction by Using of Artificial Intelligence Algorithms

Document Type : Original Article

Authors
Morteza Delkhosh 1
Mohsen Hamidian 2
Ehsan Kamali 3
1 Ph.D Student of Accounting, Najafabad Branch, Islamic Azad University, Najafabad, Iran.
2 Department of Accounting, South Tehran Branch, Islamic Azad University, Tehran, Iran.
3 Department of Accounting, Najafabad Branch, Islamic Azad University, Najafabad, Iran.
10.22034/ijfma.2025.78327.2237
Abstract
The present study has analyzed the identification of factors affecting fraudulent reporting by managers and providing a model for its prediction using artificial intelligence algorithms. In terms of approach, this research is exploratory in the qualitative part and descriptive-causal in the quantitative part. In order to achieve the research objectives, first, using theoretical foundations and opinions of experts in the field, the dimensions, components, and indicators affecting fraudulent reporting by managers were extracted and, using the Delphi method, 7 dimensions (including organizational factors, weak corporate governance, inappropriate information disclosure, poor earnings information content, poor financial performance, inefficient board of directors, and high risk-taking) and 26 components were identified and agreed upon. The statistical population of this study was all certified public accountants in Iran, and the required data were collected by distributing a researcher-made questionnaire among 338 people. After that, Inferential statistical methods such as t-test and structural equation modeling using SmartPLS were used to analyze the data. The first result of this research is the identification of dimensions and components affecting managers' fraudulent reporting, which was achieved by using theoretical studies, summarizing the opinions of accounting and auditing experts, analyzing the opinions of the statistical community, and receiving the opinions of experts in fields related to the research. The findings related to artificial intelligence algorithms stated that in general, Bayesian network algorithms, SVM algorithm, and RBF kernel algorithm have the ability to predict fraudulent reporting.
  • Asha, RB, Suresh, Kumar KR. Credit card fraud detection using artificial neural network.(2021). Global Transitions Proceedings23 January 2021.
  • Babaei, F, Rahmani, A, Homayoun, S,, Amin, V.‌(2022).‌Relationship between corporate sustainability performance and firm value: Emphasizing the role of disclosure privilege and firm size. Accounting Knowledge Journal, 12 (3), 1-27 (in Persian).
  • Chan, Yang, Tu,  Lan, Chaekuk, Na.(2024). Optimizing network locations of weigh-in-motion stations: A multi-objective approach for enhancing infrastructure management. Transportation Research Interdisciplinary Perspectives, January 2024.
  • Hashemi Bahrman, M, Pourzamani, Z, Bani Mahd, B.‌(2024).‌The Role of Decision-Making Styles in Opportunistic Earnings Management. Management Accounting and Auditing Knowledge, Volume 13 / Issue 2 (Continued 50) / Summer 2024 (in Persian).
  • Jingyu, LiCe, Guo, Xiaolong, Zheng.(2024). Financial fraud detection for Chinese listed firms: Does managers' abnormal tone matter? Emerging Markets Review, September 2024
  • Jonathan, Gilbert.(2024).‌Scripting mortgage fraud for the motion picture: “Fraud and its interrelationship with the financial services sector in the UK”. Journal of Economic Criminology, September 2024.
  • Kaveh, A, Rahnamae Roodpashti, F, Nikomaram, H.‌(2024).‌Relationships between narcissism, individual accountability and fraudulent financial reporting. Management Accounting and Auditing Knowledge, ‌ doi: 10.22034/jmaak.2024.77830.4346 (in Persian).
  • Nouruzi, F‌.‌(2015).‌Board Gender Diversity and Investment Inefficiency with the Moderating Role of Board Independence and CEO Tenure. Master's Thesis, Hakim Toos Institute of Higher Education, Accounting Department (in Persian).
  • Richard, Henderson. Using graph databases to detect financial fraud.(2020).Computer Fraud & Security, Volume 2020, Issue 7, July 2020, Pages 6-10.
  • Santosh, K., Andrew, M., Suman, N., & Chandra, T. (2018). Corporate governance reform and risk-taking: Evidence from a quasi-natural experiment in an emerging market[J]. Journal of Corporate Finance, 61, 103996.
  • Sheikhzad A, Lida, P, Omid, T, A.‌(2024).‌The effect of corporate governance quality and contradictory financial incentives on the quality of fraudulent financial reporting. Management Accounting and Auditing Knowledge (in Persian).
  • Sutharson,  Kanapathippillai, Premkanth, Puwanenthiren, Man, Dang.(2024). Board sub-committee effectiveness, director attraction and director attrition: Do nomination and remuneration committees matter? Pacific-Basin Finance Journal, September 2024.
  • Yuh-Jen, Chen, Wan-ChingLiou, Yuh-MinChen, Jyun-HanWu. Fraud detection for financial statements of business groups.(2019).International Journal of Accounting Information Systems, Volume 32, March 2019, Pages 1-23