Bibliometric analysis of artificial intelligence trends in auditing and fraud detection

Abstract

This research identifies trends in artificial intelligence (AI) in auditing and fraud detection using a combination of two methods: a bibliometric and a systematic review of AI trends in auditing in fraud detection. This research develops a bibliometric analysis of 1,348 papers on “fraud”, “auditing”, and “artificial intelligence” from 1986 to 2022. The results provide a robust set of information for in-depth research on AI trends in auditing and security detection. They not only demonstrate that there is growing academic interest in the research topic of fraud but also show clear evidence that the words “fraud”, “crime”, and “fraud detection” were the most cited, generating a great impact in the literature and developing concern with the topic. Our analysis suggests that the application of AI allows for greater facilitation of procedures to combat fraud and irregularities in the field of criminal justice and fundamental rights. Most technological changes increase ethical motivations to deter fraud, and these changes will lead to a long-term decrease in the incidence of fraud (Karpoff, 2021). This research contributes to AI valuing in audit procedures to detect and prevent fraud and simultaneously mitigate it. It also contributes to the literature, highlighting trends in AI, auditing and fraud detection, thereby enabling the development of professional judgment on the topic and providing direction for future investigations.