Two decades of financial statement fraud detection literature review; combination of bibliometric analysis and topic modeling approach
| Date | 19 April 2023 |
| Pages | 1367-1388 |
| DOI | https://doi.org/10.1108/JFC-09-2022-0227 |
| Published date | 19 April 2023 |
| Subject Matter | Accounting & finance,Financial risk/company failure,Financial crime |
| Author | Milad Soltani,Alexios Kythreotis,Arash Roshanpoor |
Two decades of financial
statement fraud detection
literature review; combination of
bibliometric analysis and topic
modeling approach
Milad Soltani and Alexios Kythreotis
Department of Accounting, Economics and Finance, European University Cyprus,
Nicosia, Cyprus, and
Arash Roshanpoor
Department of Computer, Yadegar-e-Imam Khomeini (RAH), Janat-abad Branch,
Islamic Azad University, Tehran, Iran
Abstract
Purpose –The emergence of machine learning has opened a new way for researchers. It allows them to
supplement the traditional manual methods for conducting a literature review and turnin g it into smart literature.
This study aims to present a frameworkfor incorporating machine learning into financial statement fraud (FSF)
literature analysis. This framework facilitates the analysis of a large amount of literature to show the trend of the
field and identify the most productiveau thors,journals and potential areas for future research.
Design/methodology/approach –In this study, a framework was introduced that merges bibliometric
analysis techniques such as word frequency, co-word analysis and coauthorship analysis with the Latent
Dirichlet Allocationtopic modeling approach. This framework was used to uncover subtopicsfrom 20 years
of financial fraud research articles. Furthermore, the hierarchical clustering method was used on selected
subtopicsto demonstrate the primary contexts in the literature on FSF.
Findings –This studyhas contributed to the literature in two ways.First, this study has determined the top
journals, articles, countries and keywords based on various bibliometric metrics. Second, using topic
modelingand then hierarchy clustering, this studydemonstrates the four primary contexts inFSF detection.
Research limitations/implications –In this study, the authors tried to comprehensively view the
studies related to financialfraud conducted over two decades. However,this research has limitations that can
be an opportunity for future researchers.The first limitation is due to language bias. This study has focused
on English language articles,so it is suggested that other researchers consider other languages as well.The
second limitation is causedby citation bias. In this study, the authors tried to showthe top articles based on
the citation criteria. However, judging based on citation alone can be misleading. Therefore, this study
suggests that the researchers consider othermeasures to check the citation quality and assess the studies’
precisionby applying meta-analysis.
Originality/value –Despite the popularity of bibliometric analysis and topic modeling, there have been
limited efforts to use machine learning for literature review. This novel approach of using hierarchical
© Milad Soltani, Alexios Kythreotis and Arash Roshanpoor. Published by Emerald Publishing
Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence.
Anyone may reproduce, distribute, translate and create derivative works of this article (for both
commercial and non-commercial purposes), subject to full attribution to the original publication and
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legalcode
Topic
modeling
approach
1367
Journalof Financial Crime
Vol.30 No. 5, 2023
pp. 1367-1388
EmeraldPublishing Limited
1359-0790
DOI 10.1108/JFC-09-2022-0227
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/1359-0790.htm
clustering on topic modeling results enableus to uncover four primary contexts. Furthermore, thismethod
allowedus to show the keywordsof each context and highlight significant articleswithin each context.
Keywords Detection of financial fraud, Latent Dirichlet allocation, Topic modeling,
Smart literature review, Bibliometric analysis
Paper type Research paper
1. Introduction
1.1 Description of the research topic
Almost 20 years have passed since the financial crisis caused by Enron’sfinancial fraud.
Despite significant reforms such as the Sarbanes Oxley Act and the creation of the Public
Company Accounting Oversight Board, which oversees auditors’quality, the news of
managers cooking the books continues. Even the legendary investor, the Oracle of Omaha,
has fallen victim to financial fraud (Kollewe, 2014). To combat this issue, experts suggest
financial insurance (Economist,2014) and the use of blockchain technology to revolutionize
financial transactions (Aicpa.org, 2020). Providing these examples, one may ask, what are
the causes of the financial fraud cycle? How caninvestors protect their capital by examining
the quality of their financial statements? Committing financial statement fraud (FSF) is an
unethical decision-making process, and detecting fraud is challenging because of its
multifaceted nature. For example, 50% of fraud seen by SEC enforcement has been due to
revenue recognition (e.g. cookie jar), and 35% is because of concealed liabilities and
expenses. The rest is caused by inadequate disclosure in footnotes (Association of Certified
Fraud Examiners, 2017).
According to the Association of Certified Fraud Examiners in 2020, three major frauds are
corruption, asset misappropriation and FSF. FSF schemes are the least common with the most
damaging types of fraud. FSF is the “deliberate misrepresentation of thefinancial condition of the
company accomplished through the intentional misstatement or omission of amount or
disclosures of the financial statement”to decisive financial statement users. Therefore, in this
study, we intend to review FSF detection to provide a comprehensive picture of the development
over 20 years. The applications of the present study can be summarized as follow:
Uncovering the new trend of articles by analyzing the keywords over two decades;
Grouping of the authors based upon the similarity of the content of articles;
Providing the top countries’and journals’collaboration in FSF detection literature; and
Explaining the primary contexts of FSF literature and providing the high-impact
articles in each content based on citation rate.
Therefore, the results of this study will appeal to those interested in comprehensively exploring
the FSF detection literature, such as auditors, investors, financial managers and researchers.
2. Literature review
2.1. A review of previous research
In this article, we use a combination of two approaches, bibliometric analysis and
topic modeling, to review the research literature on detecting financial fraud.
Bibliometric analysis is a quantitative method that we use to analyze a large number
of financial fraud studies and uncover emerging trends in FSF detection. This
approach is in line with the guideline presented in the article by Donthu et al. in 2021,
and the software we use is VOSviewer. Table 1 shows the conditions required to use
bibliometric analysis.
JFC
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