A Bibliometric Analysis of the Financial Fraud Detection Literature from 2004 To 2024

This paper examines the literature on financial fraud detection, using a bibliometric analysis of 386 documents published between 2004 and 2024, utilizing the Bibliometrix R package. The documents were sourced from 218 outlets including books, conferences, and journals, by searching the Scopus database using mesh terms with the Boolean operators “AND” and “OR” to combine and include synonyms of relevant terms. The content analysis identified 1186 author keywords and 530 Keywords Plus. The dataset consists of 842 authors with a collaboration rate of 18.65 and an average citation per document of 15.49. Publication trends show a significant increase in research on financial fraud detection, with key sources including the Journal of Financial Crime and the Journal of Money Laundering Control. Influential authors include Gottschalk P and Naheem MA, with major affiliations such as BI Norwegian Business School and Beijing Normal University. Keyword analysis highlights "financial crime" and "fraud" as main focuses, with technologies like "machine learning" and "artificial intelligence" playing crucial roles in fraud detection. Thematic mapping indicates that advanced technologies complement traditional methods in detecting and preventing fraud, with blockchain and cryptocurrency emerging as promising areas for future research. The United States, China, and the United Kingdom are the leading contributors to this research field.