Forensic Accounting in a Digital Environment

Abstract

In the rise of digital transformation and big data, accounting and auditing professions are required to adopt advanced techniques that help detect irregularities and frauds. This chapter collects 1,112 documents from Scopus database published in 288 sources over the period 2008-2021. A bibliometric analysis is used to depict trends, and applicable methodologies are adopted to build a comprehensible base that will serve to instill a new fraud detection model. More specifically, RStudio through “biblioshiny package”, VOSviewer, and Excel are the tools embraced to analyze the dataset information. The chapter explores the literature growth over time and addresses key aspects of the literature, such as most relevant documents, authors, countries, citation analysis. This chapter applies a network and content analysis using the bibliographical coupling, trend analysis, word cloud, and co-occurrence analysis. The theoretical model helps auditors, forensic accountants, top managements, analysts, and policy makers predict potential anomalies and misstatements.