ANALYZING THE EFFECTIVENESS OF EXTERNAL AND INTERNAL AUDITORS IN DETECTING FRAUD WITH ARTIFICIAL INTELLIGENCE SUPPORT: A SYSTEMATIC LITERATURE REVIEW

Abstract

Fraud is a serious threat to both public and private sectors, requiring the effective role of auditors in its detection. This study aims to examine the various factors influencing the ability of internal and external auditors to detect fraud, as well as to evaluate the development of modern audit technologies that support the detection process. The research employs a Systematic Literature Review (SLR) method using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) approach, which involves identifying, selecting, and analyzing 23 relevant academic articles. The results show that independence, professionalism, professional skepticism, auditor experience, and the effectiveness of internal controls are key factors in successful fraud detection. Additionally, psychological aspects such as self-efficacy and professional ethics further strengthen auditor effectiveness. The use of technologies such as artificial intelligence, big data, and statistical models has been proven to improve detection efficiency, although challenges related to auditor competence and AI interpretability remain. This study concludes that enhancing auditor competencies, adopting adaptive audit technologies, and implementing accountability-based institutional reforms are essential in addressing audit challenges in the digital era.