Journal of Industrial and Systems Engineering

Journal of Industrial and Systems Engineering

Explaining the Conceptual Model of Financial Fraud Detection Based on Transparency and Financial Discipline Using Artificial Intelligence

Document Type : Research Paper

Authors
Zeinab Nateghi Rostami
Roya Darabi
Zohreh Hajihah
Department of Accounting, ST.C., Islamic Azad University, Tehran, Iran
Abstract
This study develops and validates a conceptual model for detecting financial fraud in the financial reporting of firms listed on the Tehran Stock Exchange, emphasizing transparency and financial discipline through artificial intelligence. Based on established theoretical foundations, the model incorporates auditing, corporate governance, managerial, and financial indicators as the principal determinants of fraudulent reporting. Panel data covering the period 2013–2024 were collected and labeled using the adjusted Beneish M-Score (Adj-M-Score). Both conventional statistical methods and machine learning algorithms were applied to assess predictive performance. The results demonstrate that tree-based models, particularly XGBoost, achieve the highest predictive accuracy (AUC ≈ 0.85). Feature importance and SHAP analyses indicate that governance- and behavior-related variables, together with liquidity indicators such as the current ratio and operating cash flow to total assets, are the most influential predictors of fraud. Overall, integrating behavioral, financial, and governance dimensions within an explainable AI framework provides a robust and effective approach for improving financial transparency and detecting fraudulent reporting.
Keywords
Financial Fraud
Transparency
Financial Discipline
Corporate Governance
Artificial Intelligence
Machine Learning
Subjects
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Journal of Industrial and Systems Engineering
Volume 18, Issue 1
Winter 2026
Pages 105-116

  • Receive Date 05 February 2025
  • Revise Date 02 March 2025
  • Accept Date 16 April 2025