An intelligent quantitative auditing framework for combating bribery in the public sector: comparative evidence from Egypt and global models

Purpose and Design: This research develops an innovative Intelligent Quantitative Auditing Framework (IQAF) to enhance the detection and prevention of bribery within public sector institutions. It integrates data-driven auditing techniques, digital assurance analytics, and AI-based predictive mechanisms to strengthen the accountability systems in Egypt’s public sector. The framework is designed to align with international best practices observed in advanced and emerging economies. Methods and Approach: A mixed-method approach is employed, combining quantitative modeling through regression and risk scoring with qualitative analysis of case studies from Egypt, Singapore, South Korea, and the United Kingdom. Data were collected from auditors, regulatory bodies, and public procurement departments to validate the proposed model empirically. Findings: The results reveal that digital audit integration, auditor independence, institutional coordination, and legal framework strength significantly enhance bribery detection efficiency. Moreover, the intelligent audit model improves early warning capacity and reduces the average detection time by 35% compared to traditional audit methods. Originality and Value: The study provides the first integrated quantitative auditing model explicitly tailored to combating bribery in the public sector. It bridges technological, legal, and professional perspectives, providing actionable insights for national anti-corruption strategies. Theoretical, Practical, Economic, and Social Implications: The research advances auditing theory by embedding predictive analytics in assurance processes; it supports practical anti-bribery policy reforms, enhances fiscal integrity, and reinforces citizens’ trust in government operations.