Strategy Model for Detecting Potential Fraudulent Financial Statements of State-Owned Enterprises in Indonesia: Fraud Heptagon Theory Analysis with Big Data Analytics, Risk Management, and Sustainable Development Goals (SDGS)

Objective: Fraudulent financial statements are among the least frequent types of fraud globally but result in the highest median losses for companies. This study aims to develop a strategy model for detecting potential fraudulent financial statements in Indonesia’s State-Owned Enterprises (B.U.M.N.) by analyzing the Fraud Heptagon Theory, which includes stimulus, ability, collusion, opportunity, rationalization, ego, and big data analytics. Theoretical Framework: The study incorporates risk management and the audit committee as moderating variables to evaluate their roles in mitigating fraudulent financial reporting. Fraudulent financial statements were measured using the F-Score Model, emphasizing Goal 16 of the Sustainable Development Goals (SDGs): Peace, Justice, and Strong Institutions. Method: A total of 126 samples from 2018–2022 were obtained through purposive sampling. Data analysis employed Warp PLS 7.0 with a Structural Equation Modeling (SEM-PLS) approach. Results and Discussion: The findings reveal that most Fraud Heptagon Theory elements do not significantly affect fraudulent financial statements, except for financial pressure, which acts as a stimulus proxy. While big data analytics enhances risk management, it does not directly mitigate fraudulent reporting. The audit committee moderates the collusion relationship with fraudulent financial reporting, albeit with limited overall impact. Research Implications: This study offers a framework for stakeholders to leverage innovative technologies and governance strategies to improve financial transparency and accountability, aligning corporate practices with SDGs. Originality/Value: This research integrates the Fraud Heptagon Theory with big data analytics and SDGs, proposing a novel strategy model for detecting fraudulent financial statements in State-Owned Enterprises. It highlights the dynamic role of advanced technologies and governance in fraud detection.