A quantum-inspired cognitive predictive–diagnostic framework for auditors’ responsibilities in detecting corruption: evidence from developed and developing economies

Purpose and Design: This study develops and empirically validates a quantum-inspired cognitive predictive–diagnostic framework that re-engineers auditors’ responsibilities in detecting corruption across developed and developing economies. Building upon agency theory, accountability theory, and cognitive–behavioral auditing models, the framework distinguishes between predictive responsibilities (anticipating corruption risk ex-ante) and diagnostic responsibilities (tracing anomalies ex-post) within a unified decision environment. Methods and Approach: Using cross-country public-procurement and audit datasets compliant with the Open Contracting Data Standard (OCDS) and engineering-audit records from 2015–2024, the study integrates machine-learning classification with a quantum-inspired optimization algorithm that allocates audit procedures under time and cost constraints. Panel regressions and difference-in-differences tests assess whether the framework enhances corruption detection efficiency and accountability outcomes. Findings: Results reveal that the quantum-inspired predictive–diagnostic model improves corruption detection rates by 27–35 % and reduces audit resource dispersion by 18 %, with stronger effects in developing economies where traditional control systems are weaker. Auditors applying the framework exhibit higher cognitive adaptability and professional skepticism in complex environments. Originality and Value: This is the first study to operationalize quantum-inspired cognition within auditing responsibilities, bridging behavioral, technological, and institutional dimensions of corruption detection. Theoretical, Practical, Economic, and Social Implications. Theoretically, the framework extends the responsibility–accountability nexus under uncertainty; practically, it offers audit regulators and supreme audit institutions a scalable digital model; economically, it optimizes audit costs; and socially, it strengthens integrity and trust in public financial management.