THE ROLE OF ARTIFICIAL INTELLIGENCE METHODS IN REDUCING INTERNAL CONTROL RISKS: SYSTEMATIC LITERATURE REVIEW

Abstract Background: Cases of financial fraudulence have persistently been reported in the audit systems of various financial sectors across the globe despite the promising role of Artificial Intelligence in reducing risks associated with financial audit practices. As such, an apparent gap exists in the current literature how artificial intelligence can be used to manager internal control risks. To explore the role of artificial intelligence in reducing internal control risks, a systematic review was conducted by seeking recent articles published three scholarly journal sites, including EBSCO Business Source Complete, ProQuest Central, and Wiley Online Library. Additional search also conducted on Google Scholar and reference lists of other articles to ensure a complete search. Articles published between 2019 and 2024, in English and having empirical evidence were included. Data was extracted using data abstraction table before being analysed through narrative approach for qualitative and quantitative findings. 3,447 articles were extracted from the literature search from the journal databases and other sources. However, only 10 articles found fit the set criteria and relevance. The articles’ screening process summarized in the PRISMA diagram. Two thematic outcomes were noted; the significance of AI application in reducing fraud and internal control risks through different approaches such as improving auditors’ efficiency, and the challenges against the successful use of AI in internal risk control, such as policy and structural barriers. Artificial intelligence is a critical tool for improving the efficiency of financial audit functions; however, requires more organizational input to overcome the inherence challenges, such as inadequate employees’ knowledge and skills.