Unveiling the Core Dimensions of Financial Statement Fraud: An In-depth Bibliometric Review

Abstract

ABSTRACT
Manuscript type: Research paper
Research aims: The study aims to filter for core publications based
on bibliometric coupling and to identify theories, determinants,
measurements, and attributes (TDMA) from the literature through a
mapping analysis of Financial Statement Fraud (FSF).
Design/Methodology/Approach: This study identifies TDMA in the
FSF literature through a mapping analysis of the centrality and impact
of each cluster. It has used a two-stage analytical framework combining
bibliometric coupling (via the Bibliometrix 4.1 R package using the
Louvain clustering algorithm) and TDMA-based systematic mapping. A
total of 521 Scopus-indexed journal articles were analysed, and the top
15 publications from each cluster were evaluated based on centrality and
impact scores to reveal core research patterns and interconnections.
Research findings: Four clusters of FSF were identified. Cluster 1
provides the fundamental theoretical background of FSF, including its
deductions and predictions. Cluster 2 examines the relationship between
essential governance practices, earnings management, and FSF literature.
Cluster 3 presents contemporary artificial intelligence (AI) and machine
learning (ML) approaches for accurately forecasting FSF, and Cluster 4
offers exploratory research to analyse FSF.
Theoretical contribution/Originality: The findings contribute to the fields
of accounting, auditing, and finance. Their novelty lies in the dynamic
structure used to explain multiple clusters of FSFs by mapping them to
the TDMA literature.
Practitioner/Policy implications: The findings emphasise the need
for integrated fraud-prevention frameworks that combine traditional
governance mechanisms with AI-driven analytical tools. Policymakers,
regulators, and auditing bodies can leverage these insights to enhance
transparency, strengthen internal controls, and design proactive fraud
detection systems that align with international accounting standards.