ARTIFICIAL INTELLIGENCE INTERVENTION IN AUDITING AGAINST FUND EMBEZZLEMENT IN THE BANKING SECTOR OF NIGERIA

This study investigates the role of Artificial Intelligence (AI) in enhancing internal auditing mechanisms to prevent and detect fund embezzlement within the Nigerian banking sector. The primary objective is to evaluate how AI-driven audit systems contribute to mitigating fraud risks while addressing the organizational and human factors that shape their effectiveness. A qualitative research design was employed, combining semi-structured interviews with internal auditors from selected Nigerian banks and direct observations of AI-enabled audit systems. Data were analyzed thematically using coding techniques, and the findings were interpreted through the lenses of Fraud Triangle Theory, Agency Theory, and Technology Adoption frameworks. Results reveal that AI strengthens audit effectiveness by improving anomaly detection and reinforcing internal controls, thereby narrowing opportunities for fraud. However, the study highlights that pressures and rationalizations driving fraudulent behaviors remain persistent challenges beyond the scope of technology. Additionally, the adoption and effectiveness of AI are influenced by organizational readiness, auditor competence, and cultural attitudes within the banking sector. This research contributes both theoretically and practically by integrating multiple perspectives to explain fraud dynamics in developing economies, emphasizing that technology alone cannot eliminate embezzlement risks without complementary regulatory, cultural, and human resource interventions.