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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 turning it into smart literature. This study aims to present a framework for 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 productive authors, journals and potential areas for future research.

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 Allocation topic modeling approach. This framework was used to uncover subtopics from 20 years of financial fraud research articles. Furthermore, the hierarchical clustering method was used on selected subtopics to demonstrate the primary contexts in the literature on FSF.

This study has 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 modeling and then hierarchy clustering, this study demonstrates the four primary contexts in FSF detection.

In this study, the authors tried to comprehensively view the studies related to financial fraud 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 caused by citation bias. In this study, the authors tried to show the top articles based on the citation criteria. However, judging based on citation alone can be misleading. Therefore, this study suggests that the researchers consider other measures to check the citation quality and assess the studies’ precision by applying meta-analysis.

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 clustering on topic modeling results enable us to uncover four primary contexts. Furthermore, this method allowed us to show the keywords of each context and highlight significant articles within each context.

The purpose of this research is to obtain empirical evidence about the impact of auditor’s integrity and organizational commitment to the prevention of fraud.

This research was conducted using questionnaires distributed to all internal auditors who worked at East Java Representatives Office of Indonesia’s National Government Internal Auditor. One hundred and thirteen questionnaires were distributed, and fifty-seven questionnaires were received, and all have validity eligible to use in this research. The hypothesis of this research was tested using the partial least square analysis with WarpPLS version 6.0 software.

The result of this research found that auditor’s integrity and organizational commitment affect positively to fraud prevention.

Using government internal auditors, this study believed, brings a new insight into government internal auditor behavior.

This study provides in-depth explanation of Goods and Services Tax (GST) fraud prevention towards sustainability business.

This study applies a qualitative research method, i.e. case study, to address the specific research objective.

The finding revealed a GST prevention model towards sustainable business. The finding shows that it is pertinent for the government to set preventive strategies in order to retain sustainable income for the government. Two essential dimensions emerged in the findings to support preventive strategies, namely macro- and micro-level measures.

The findings of this study provide managers, investors and policymakers with evidence to what extent GST fraud could be minimize in order to safeguard government source of revenue and retain sustainable business in a country. As GST is an important source of revenue for the government, it is thus crucial to prevent fraud from occurring.

Past studies have primarily focused on GST implementation from the perspective of service tax effectiveness and efficiency. However, this study examined the impact of GST fraud to determine measures that could ensure service tax sustainability using preventive strategies, in turn, introducing to the existing literature on indirect tax.

This study reviews and discusses the accounting literature that analyzes the role of auditors and enforcers in the context of fraud.

This literature review includes both qualitative and quantitative studies, based on the idea that the findings from different research paradigms can shed light on the complex interactions between different financial reporting controls. The authors use a mixed-methods research synthesis and select 64 accounting journal articles to analyze the main proxies for fraud, the stages of the fraud process under investigation and the roles played by auditors and enforcers.

The study highlights heterogeneity with respect to the terms and concepts used to capture the fraud phenomenon, a fragmentation in terms of the measures used in quantitative studies and a low level of detail in the fraud analysis. The review also shows a limited number of case studies and a lack of focus on the interaction and interplay between enforcers and auditors.

This study outlines directions for future accounting research on fraud.

The analysis underscores the need for the academic community, policymakers and practitioners to work together to prevent the destructive economic and social consequences of fraud in an increasingly complex and interconnected environment.

This study differs from previous literature reviews that focus on a single monitoring mechanism or deal with fraud in a broadly manner by discussing how the accounting literature addresses the roles and the complex interplay between enforcers and auditors in the context of accounting fraud.