Presenting a Tax Evasion Prediction Model from the Perspective of Fraudulent Financial Reporting Among Taxpayers Using Grounded Theory
Keywords:
fraudulent financial reporting and data, based theory, Tax evasion predictionAbstract
Objective: This study aimed to develop a comprehensive model for predicting tax evasion from the perspective of fraudulent financial reporting among taxpayers through a grounded theory approach.
Methodology: This applied qualitative study employed grounded theory methodology. The participants consisted of 24 academic experts, tax consultants, auditors, and tax inspectors selected through snowball sampling. Data were collected using semi-structured interviews and continued until theoretical saturation was achieved. The interview data were analyzed using MAXQDA 2020 software through open, axial, and selective coding procedures based on the Strauss and Corbin framework.
Findings: The analysis generated 94 initial concepts and 25 categories, leading to the development of a paradigmatic model of tax evasion prediction. The findings revealed that the core category, “tax evasion prediction from the perspective of fraudulent financial reporting,” is shaped by causal conditions including pressure factors, opportunities, rationalization mechanisms, fraudulent financial reporting practices, and environmental–legal factors. Intervening conditions involving technological, behavioral, economic, and ethical dimensions influence the process. Strategic mechanisms such as data analytics, artificial intelligence, machine learning, anomaly detection, benchmarking, and financial statement red-flag analysis were identified as key tools for strengthening tax evasion prediction. The model outcomes include improved economic indicators, enhanced social outcomes, stronger governance, and more effective tax evasion control.
Conclusion: The results indicate that tax evasion is a multidimensional phenomenon influenced by interconnected organizational, behavioral, environmental, technological, and legal factors. The proposed model provides a practical framework for designing intelligent tax evasion prediction systems and developing preventive tax policies. Furthermore, integrating grounded theory insights with advanced analytical technologies can significantly enhance the effectiveness of tax administration and fraud detection mechanisms.
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