A Bibliometric Analysis On The Use Of Machine Learning İn Auditing (Third Sector Social Economic Review, 2025)
This study aims to identify the most appropriate machine learning methods used in the field of auditing and to reveal the contributions of these methods to the field of auditing. In this context, a bibliometric analysis method was used. Within the scope of this analysis, 246 studies were examined, including 147 from the Scopus database and 99 from the Web of Science database. Following the combination of data, 160 studies were selected for comprehensive analysis. The analysis of bibliometric data was conducted using the Bibliometrix package in the R-Studio program with the help of Biblioshiny. The findings show that logistic regression and linear regression analysis continue to be widely used machine applications in auditing since the 1980s. In addition, the analyses show that in recent years, advanced applications such as deep learning, Long Short-Term Memory, Beetle Antennae Search, random forests, and XGBoost are more preferred. This study provides valuable insights for future researchers aiming to develop a comprehensive roadmap for conducting research on machine learning in the field of auditing. The study also emphasizes the necessity of approaching auditing within the discipline of accounting but from an interdisciplinary perspective, thereby contributing new perspectives to the literature in this context.
Keywords: Accounting; Auditing; Machine Learning; R-Studio; Bibliometrics