IMPLICATIONS OF ADOPTING ARTIFICIAL INTELLIGENCE ON THE INTERNAL AUDIT PROCESS TO IMPROVE FINANCIAL FRAUD DETECTION

Abstract

This study explores the impact of Artificial Intelligence (AI) adoption on the effectiveness, efficiency, and accuracy of internal audits in detecting financial fraud. Using a mixed-methods approach, surveys and in-depth interviews were conducted with internal auditors to assess AI's effectiveness and explore challenges, perceptions, and risks. The findings indicate that AI technologies, such as machine learning, data mining, and natural language processing, improve fraud detection by identifying patterns and anomalies that traditional methods cannot. AI also accelerates real-time data analysis, reduces human bias, and enhances surveillance of suspicious activities. However, challenges such as limited organizational readiness, technical expertise, and algorithmic bias were identified. The study concludes that while AI offers significant improvements in fraud detection, successful implementation depends on technology integration, auditor training, and effective risk management strategies. Practical recommendations include enhancing AI implementation frameworks and supporting digital transformation in auditing.