The Moderate Role of National Culture and Prosperity index on the Effectiveness of the Fraud Triangle to Prevent Financial Statement Fraud: Machine learning and Meta-Analysis Approach

The Enron scandal, a significant event almost twenty years ago, intensely impacted the financial sector, revealing the severe consequence of unregulated corporate misbehavior. Following this catastrophe, major reforms were introduced, such as implementing the Sarbanes-Oxley Act and creating the Public Company Accounting Oversight Board (PCAOB).
These measures aimed to restore confidence in financial markets. However, despite these changes, financial and accounting deceptions continue to occur, creating doubt about the reliability of corporate financial reports. Prominent investors, such as Warren Buffett, known as the Oracle of Omaha, have also been affected by financial fraud. This highlights the persistent difficulties in detecting and preventing financial fraud. It underscores the importance of comprehensively grasping the root causes of financial fraud. The main goal of the current study is to provide a framework to explore the risk factors (i.e., pressure, opportunity, and rationalization) within the domain of financial statement fraud (FSF) with a focus on how national culture and prosperity indices moderate this relationship. The research faces three primary challenges: The complexity of financial fraud, which arises from the multifaceted aspects of fraudulent practices; the high volume of existing (FSF) literature; and the diverse results across studies.
To address these challenges, this study employs three interrelated methodological approaches: Bibliometric analysis, Systematic review, and Meta-analysis, each tailored to address distinct challenges. Bibliometric Analysis addresses the challenge of the overwhelming volume of literature by mapping the landscape of (FSF) research. This method systematically evaluates trends, author groupings, country contributions, and key journals using quantitative measures like citation counts and keyword co-occurrences. The use of machine learning, specifically Latent Dirichlet Allocation (LDA) topic modeling, uncovers emerging trends and thematic
clusters within two decades of (FSF) research, providing a structured overview of the research domain. Systematic Review tackles the complexity of financial fraud by critically evaluating
and synthesizing existing studies according to predefined criteria. This approach filters out irrelevant studies and focuses on high-quality research that directly investigates (FSF) risk factors. Meta-analysis addresses the heterogeneity of research findings by quantitatively combining results from individual studies. This method calculates effect sizes and by
aggregating data across studies, meta-analysis harmonizes disparate findings and enhances the reliability and validity of conclusions drawn about the (FSF) landscape. Additionally, countries globally have been clustered based on their fraud scores, using the Corruption Perception Index (CPI) as an indicator, along with cultural Hofstede scores and prosperity indices. These results help managers understand how various cultural and economic factors influence the risk of financial fraud in different regions. By recognizing these moderating effects, managers can tailor their fraud prevention and detection strategies to be more effective, considering the specific attributes of the countries in which they operate.
Based on this explanation, the current study contributes to the literature in four ways. First, this study pioneers the combination of bibliometric analysis with Latent Dirichlet Allocation (LDA) topic modeling in the field of finance. This integration helps uncover emerging trends and thematic clusters within two decades of FSF research, providing a structured and innovative
way to analyze a vast amount of literature. Second, this study identifies key risk factors associated with (FSF), both quantitatively and qualitatively, as a comprehensive framework to detect and mitigate fraud occurrence. This allows for a more precise estimation of the effectiveness of the fraud risk factors concerning (FSF). Third, the study provides insights into the moderate effect of country-specific features, including national culture and prosperity
factors, on (FSF) occurrence. These insights contribute significantly to behavioral forensics, a subfield of forensic accounting that focuses on understanding human aspects. Examining these country-specific factors helps gain a deeper understanding of the complex interplay between human behavior and the environment within financial fraud. Fourth, this study clusters countries globally based on their fraud scores, cultural dimensions, and prosperity indices. This clustering helps to identify patterns and differences in how various countries' features moderate the financial risk factors and detection of financial fraud across different regions.
Recognizing these moderating effects will enable managers to develop more effective fraud prevention and detection strategies tailored to the cultural and economic attributes of different countries. This will enhance global efforts to reduce financial fraud.
Keywords: Fraud Triangle, SAS No. 99, Meta-Analysis, Bibliometric Analysis, Topic Modeling, Smart Literature Review.
Declaration of Work: This thesis represents my original work, embodying a genuine effort in scholarly research. I take full responsibility for its content and conclusions.