State of the art in financial statement fraud detection: A systematic review

Abstract

Over the past few decades, fraud has been increasingly prevalent, with large businesses like Satyam, Enron, and WorldCom making headlines for their deceptive financial reporting practices. In this research, we conducted a systematic review and bibliometric analysis of the literature concerning fraud detection in financial statements. Following a bibliometric analysis, we identified the leading researchers, publications, sources, countries, and collaboration patterns in financial statement fraud detection. Our systematic review covered the following topics: the data analytics tools used, databases used to identify fraudulent firms, the design of control group samples (non-fraudulent firms), the critical dimension reduction tools used, techniques adopted to address data rarity (imbalanced data), explanatory variables used in the model, theoretical framework supporting the fraud indicators, optimization techniques used, the use of evaluation metrics, and significant findings. The systematic review followed the approach provided by Tranfield et al. (2003), and the bibliometric analysis was conducted using the VOSviewer. Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA), 2020 reporting criteria were followed for reporting the systematic review's findings. We provide a brief overview of the existing literature, drawing both conclusions and recommendations for directions in which additional study is warranted. Our results provide valuable information that can be used by future academics, auditors, enforcement agencies, and regulators as they work to create the most effective fraud detection algorithms possible.