Developing an Intelligent Internal Audit Framework Based on Machine Learning, Big Data Analytics, and Preventive Risk Controls
Keywords:
intelligent internal audit, machine learning, big data analytics, preventive controls, risk management, thematic analysisAbstract
This study aimed to develop an intelligent internal audit framework based on machine learning, big data analytics, and preventive risk controls in organizations located in Tehran. This qualitative study was conducted using thematic analysis. The participants included 18 experts in internal auditing, risk management, information technology, data analytics, and accounting/auditing academia in Tehran. They were selected through purposive and snowball sampling. Data were collected through semi-structured interviews and continued until theoretical saturation was achieved. Core saturation occurred at the sixteenth interview, and two additional interviews were conducted to confirm and stabilize the emerging categories. All interviews were transcribed verbatim, imported into NVivo, and analyzed through a six-phase thematic analysis procedure. The analysis resulted in five main categories: data-driven infrastructure and big data architecture for internal auditing, a machine-learning engine for dynamic risk assessment and prediction, preventive controls and continuous monitoring, model governance and ethical-security requirements, and redesign of internal auditors’ roles and competencies. The findings indicated that intelligent internal auditing is not limited to deploying analytical tools. Rather, it requires systematic integration of financial and non-financial data, early-warning risk indicators, human accountability over algorithmic outputs, and a redefined relationship between internal audit, senior management, the board, and the audit committee. The proposed framework suggests that intelligent internal auditing becomes effective when machine learning and big data analytics are embedded within preventive controls, data governance, model explainability, and professional audit judgment. Accordingly, organizations should move from periodic and reactive internal auditing toward continuous, predictive, and risk-oriented assurance. The framework provides a practical basis for strengthening audit quality, improving risk anticipation, reducing control failure, and increasing the strategic value of internal audit in data-intensive organizations.
Keywords: intelligent internal audit; machine learning; big data analytics; preventive controls; risk management; thematic analysis.
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References
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