Key research themes
1. How can data analytics and predictive models improve early detection of accounting fraud?
This theme explores the development and application of quantitative techniques, including financial ratio analysis, logistic regression, machine learning, and predictive analytics models, to identify early signs of accounting fraud. The importance lies in enhancing the accuracy, interpretability, and cost-efficiency of fraud detection in financial statements to prevent corporate collapses and protect stakeholders.
2. What roles do corporate governance, internal controls, and ethical compliance play in mitigating accounting fraud?
This theme investigates how frameworks of corporate governance, adherence to accounting standards, ethics, risk management, internal audit, and management support influence the incidence and mitigation of accounting fraud. It emphasizes theoretical perspectives and empirical analysis linking governance mechanisms and individual moral factors to fraud control effectiveness.
3. How do fraud investigation frameworks and emerging technologies enhance detection and prevention of accounting fraud?
This theme addresses practical approaches to fraud investigation plans, challenges in fraud detection, and the potential of technological innovations like blockchain to improve transparency and prevention. It also examines conceptual frameworks explaining why fraud remains undetected despite existing efforts and how strategic solutions can be formulated.