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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.

This study aims to investigate the association between corporate governance and financial transparency, using the moderating role of an Egyptian currency devaluation decision as a policy shock.

Data was collected for a sample of companies listed on the Egyptian stock exchange from 2014 to 2019. To control for time-invariant unobserved heterogeneity, the authors analyse panel data using an estimated generalised least squares regression model.

The findings underline the pitfalls of assuming that corporate governance mechanisms are effective regardless of circumstances and support the complementary roles of a number of theories in interpreting the empirical findings.

This study is limited to non-financial companies and includes only corporate board and audit committee governance mechanisms. The study results have important implications for policymakers, international lending institutions, investors and accounting standards setters. It is of particular importance to policymakers in other less-developed countries with similar economic conditions.

To the best of the authors’ knowledge, this study is the first empirical attempt to provide evidence of the impact of a currency devaluation shock on the relationship between corporate governance and financial transparency within the Egyptian context as an example of a transitional economy. Hence, it provides a significant theoretical and empirical contribution to the literature.