Document Type : Original Article
Authors
1
Department of Accounting, Ara.C., Islamic Azad University, Tabriz, Iran
2
Department of Accounting, Ta.C., Islamic Azad University, Tabriz, Iran
3
Department of Accounting, Sou. C., Islamic Azad University, Soufian, Iran
4
Department of Accounting, Ta. C, Islamic Azad University, Tabriz, Iran
Abstract
Financial reporting fraud is one of the most critical challenges facing financial systems, with far-reaching consequences for transparency, public trust, and the efficiency of financial markets. The complexity of banking operations, coupled with the limitations of traditional fraud detection methods, has intensified the need for comprehensive evidence-based models. This study aimed to identify and evaluate the factors influencing financial reporting fraud in banks through a mixed-methods approach. In the qualitative phase, Grounded Theory was employed, and in-depth interviews were conducted with experts in accounting and banking. The dimensions of the phenomenon were identified and categorized into causal conditions, contextual conditions, intervening conditions, the central phenomenon, strategies, and consequences, leading to the development of a conceptual model for fraud identification in the banking sector. In the quantitative phase, a researcher-developed questionnaire was designed based on the categories extracted from the qualitative findings and distributed among 450 banking managers, auditors, and other stakeholders. Data were analyzed using Structural Equation Modeling (SEM). The results revealed that causal conditions, contextual conditions, and strategies exert significant effects on the identification and consequences of fraud. However, the effects of intervening conditions and the central phenomenon were not significant in certain structural paths. Furthermore, model fit indices, including the Standardized Root Mean Square Residual (SRMR), Goodness-of-Fit (GOF), R², Q², and CVCOM, confirmed the adequacy of both the measurement and structural models as well as the predictive validity of the proposed framework.
Keywords