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Financial statement fraud has become a global concern, and auditors are increasingly focused on identifying and investigating it. Auditors may play a crucial role in investigating and reducing financial statement fraud, and this is particularly important in developing countries where fraudulent practices are more prevalent due to the lack of strict regulations and oversight. This study investigates whether enhanced audit quality has an impact on reducing financial statement fraud. The primary aim is to recognize whether a higher level of audit quality relates with a decrease in fraudulent activities in Indonesia, which is one such country that has not yet adopted IFRS.

This study investigates the effect of audit quality, as measured by audit tenure, audit fee, and audit size, on the dependent variable of financial statement fraud, as indicated by Dechow F-value. The sample for this study comprises 951 observations from 2015 to 2020, and the research design utilizes a panel data approach. To test the main hypothesis, OLS, and GMM estimation techniques are employed.

The analyses reveal a negative relationship between audit tenure and financial statement fraud. This suggests that shorter audit tenure may be associated with an increased risk of financial statement fraud. This heightened risk could stem from auditors having limited time to thoroughly understand the company's operations and internal controls, potentially making it more challenging to detect and prevent fraudulent activities perpetrated by the client. Conversely, a positive relationship is identified between audit fees and financial statement fraud, suggesting that companies paying higher fees may be engaging auditors less adept at detecting fraudulent activities. Furthermore, a negative relationship is observed between Big-5 and financial statement fraud, which may be due to the greater resources, expertise, quality control, scrutiny, reputation, and ethical conduct of Big-5 audit companies.

This study only focused on listed companies in Indonesia, therefore, caution should be exercised when generalizing the findings to other developing and Muslim countries such as Malaysia. The findings may differ due to the adoption of IFRS in Malaysia. As such, it is important for future studies to include Malaysia as a sample and compare the results with those of Indonesia. This comparison would demonstrate the impact of IFRS adoption on the relationship between audit quality and financial statement fraud and provide insights for policy makers in Indonesia.

The findings of this study have important implications for developing countries that have been shown to be more susceptible to fraud than developed countries. This study contributes to the existing research on the role of audit quality in reducing financial statement fraud and emphasizes the need for auditors and accountants to take a proactive approach in detecting and investigating financial fraud.

This study is a new study because it investigates the relationship between audit quality and financial statement fraud in Indonesia, a developing Muslim country that has not yet adopted International Financial Reporting Standards (IFRS). The study provides valuable evidence on the unique factors that influence fraud in Indonesia and fills a gap in the literature as previous studies on this topic have largely focused on developed countries. Additionally, the study recommends that policymakers in Indonesia consider implementing IFRS to improve the reliability of financial reporting and strengthen the effectiveness of the auditing process, thus reducing the incidence of fraud.

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.

According to the Association of Certified Fraud Examiners, financial statement fraud represents the smallest amount of fraud cases but results in the greatest monetary loss. The researcher previously investigated the characteristics of financial statement fraud and determined the presence of 16 fraud indicators. The purpose of this study is to establish whether investors and other stakeholders can detect and identify financial statement fraud using these characteristics in an analysis of a company’s annual report.

This study analyses a financial statement fraud case, using the same techniques that were previously applied, including horizontal, vertical and ratio analysis. These are preferred because stakeholders have relatively easy access to them.

The findings show several fraud characteristics, with a few additional ones not previously found prevalent. Financial statement fraud thus tends to differ between cases. It is also easier to detect and identify fraud indicators ex post facto.

This study is a practical case showing that financial statement fraud can be detected and identified in the financial statements of companies that commit fraud.

This study examines the association between emotional intelligence (EI) and the tendency of future accountants to rationalize and engage in occupational fraud.

The study adopts a survey methodology and uses a questionnaire containing a fraud scenario and EI construct to gather data from 225 participants. It performed a Cronbach alpha to assess the measurement parameters consistency of EI and fraud tendency and employed Pearson correlation and regression analysis to test its hypothesis.

The study found that future accountants in Nigeria are emotionally intelligent and have a high fraud tendency. Also, it found a significant and positive association between EI and fraud tendency, suggesting that future accountants that are emotionally intelligent have a higher tendency to rationalize and engage in occupational fraud. In addition, the study found that academic intelligence, a control variable, positively associates with fraud tendency.

The study offers rare insights into the fraud tendency of future accountants, which would benefit the counter fraud community in Nigeria and other developing countries. Recruiters and employers will find the study beneficial in decision-making on job recruitment, placements and moral orientation for prospective accountant employees.

The study is the first to directly associate EI with the fraud tendency of future accountants from a developing country with high fraud profile and underdeveloped counter fraud strategy. Thus, it provides a benchmark for future studies in other developing countries.

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.

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 paper aims to develop a theoretical framework to predict susceptibility to cyber-fraud victimhood.

A survey was constructed to examine whether personality, socio-demographic characteristics and online routine activities predicted one-off and repeat victimhood of cyber-fraud. Overall, 11,780 participants completed a survey (one-off victims, N = 728; repeat victims = 329).

The final saturated model revealed that psychological and socio-demographic characteristics and online routine activities should be considered when predicting victimhood. Consistent with the hypotheses, victims of cyber-frauds were more likely to be older, score high on impulsivity measures of urgency and sensation seeking, score high on addictive measures and engage in more frequent routine activities that place them at great risk of becoming scammed. There was little distinction between one-off and repeat victims of cyber-frauds.

This work uniquely combines psychological, socio-demographic and online behaviours to develop a comprehensive theoretical framework to predict susceptibility to cyber-frauds. Importantly, the work here challenges the current utility of government websites to protect users from becoming scammed and provides insights into methods that might be used to protect users from becoming scammed.

Tax evasion and tax avoidance consistently strip national budgets of tremendous financial resources. Consequently, the discussion on tax fraud remains topical and a moving target subject matter. Various antecedents and moderators of tax fraud have been investigated over the years, yet the literature dedicated to the linkage between corporate social responsibility (CSR) and tax practices exhibited ambiguous results. In this respect, the purpose of this study is to present the results of an investigation into the nonfinancial factors affecting tax fraud and the moderating effect of CSR-related behaviors.

Structural equation modeling (SEM) was applied in a multivariate statistical analysis technique to analyze structural relationships. The measurement and structural models were evaluated using component-based partial least squares (PLS), a rigorous statistical instrument. The opportunity to use PLS-SEM is supported by the advancement of models comprising both reflective and formative constructs as in the present case.

Data collected from a sample of 290 respondents from Romania confirmed that social and ethical factors significantly impact tax fraud and CSR-related behaviors. In addition, the latter plays a moderating effect between nonfinancial factors and tax fraud.

The research sample is country-centric (i.e. subjects come from Romania) while the questionnaire-based survey relies on self-reported measures.

The paper adds new evidence to the extant knowledge and points to theoretical and managerial implications.

One of the insidious effects of government and business imposter scams is the potential erosion of trust among defrauded consumers. This study aims to assess the relationship between prior imposter scam victimization and present ability to discriminate between real and fake digital communications from government agencies and retail companies.

This paper tests whether a short, interactive training can help consumers correctly identify imposter scams without mistrusting legitimate communications. Participants were randomized into one of two control groups or to one of two training conditions: written tips on identifying digital imposter scams, or an interactive fraud detection training program. Participants were tested on their ability to correctly label emails, websites and letters as real or a scam.

This paper find that prior imposter scam victimization is not associated with greater mistrust. Compared to the control conditions, both written tips and interactive digital fraud detection training improved identification of real communications and scams; however, after a two- to three-week delay, the effect of training decreases for scam detection.

Results indicate that prior imposter scam victimization is not associated with mistrust, and that one-time fraud detection training improves consumers’ detection of imposter scams but has limited long-term effectiveness.

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.