Developing a Paradigmatic Model of Intelligent Internal Auditing in Banks Based on COBIT and Artificial Intelligence: A Grounded Theory Approach

Abstract

This study aimed to develop a paradigmatic model of intelligent internal auditing in banks based on the COBIT framework and the capabilities of artificial intelligence. This applied study adopted a qualitative, exploratory, and theory-building design using the systematic grounded theory approach. The study population consisted of university academics and senior audit managers in state-owned banks with expertise in internal auditing, internal control, information technology governance, risk management, data analytics, and artificial intelligence. Theoretical sampling was implemented through purposive and snowball procedures, and semi-structured interviews continued until theoretical saturation was achieved with 12 experts. Data were collected through an interview guide containing seven open-ended questions and complementary document analysis. Credibility and dependability were enhanced through source triangulation, expert review, participant diversity, process documentation, constant comparison, and review of preliminary findings. Data were analyzed using open, axial, and selective coding. The analysis generated 187 open codes, 18 axial categories, and six major categories. In the final paradigmatic model, “COBIT-based governance integration” emerged as the causal condition; “technological infrastructure and the data ecosystem” as the contextual condition; “human empowerment and cultural transformation” as the intervening condition; “analytical intelligence and predictive assessment” as the central phenomenon; “intelligent automation and continuous auditing” as the strategic response; and “intelligent risk and compliance management” as the principal outcome. The resulting relationships indicated that COBIT-based governance provides strategic direction, data and technological infrastructure establish the operational foundation, and human and cultural capacities facilitate or constrain implementation. Analytical intelligence transforms data into audit evidence and predictive insight, while automation and continuous auditing operationalize these capabilities and ultimately support dynamic risk assessment, continuous compliance monitoring, and proactive fraud detection and prevention. Intelligent internal auditing in banks cannot be achieved merely through the deployment of isolated AI tools; rather, it requires an integrated architecture that simultaneously aligns governance, data, technology, human competencies, and auditing processes. The proposed model can provide a conceptual framework for internal audit transformation, AI-based audit initiatives, professional competency development, and more proactive risk and compliance management in banking institutions.