The Fraud Hexagon as an Analytical Framework for Predicting Financial Statement Fraud: A Systematic Literature Review

This article analyzes the Fraud Hexagon model as a framework for detecting financial statement fraud across various sectors and geographic contexts using the Systematic Literature Review (SLR) approach. The model incorporates six elements: pressure, opportunity, rationalization, capability, ego, and collusion. The study reviewed 12 SCOPUS-indexed articles focusing on the banking, manufacturing, SMEs, and infrastructure sectors in national and regional contexts. Financial statement fraud results in the highest financial losses among fraud types. The Fraud Hexagon, an evolution of earlier theories, provides a comprehensive approach to fraud detection. This study aims to evaluate the application of the Fraud Hexagon model across sectors to identify critical factors influencing fraud and offer strategic recommendations for improving internal and external controls. Using the PRISMA framework, the study employs the SLR approach to filter and analyze relevant SCOPUS-indexed literature. The study examined fraud detection through stages of keyword analysis, highlighting pressure and collusion in regulated industries. Corporate governance and audit quality enhance detection, but limitations include a lack of behavioral and sector-specific focus. Future research should integrate behavioral and cultural dynamics with advanced analytics.