Design of a Secure AI-Driven Adaptive Audit Transparency Engine to Improve Tax Compliance, Reduce Administrative Inefficiencies and Strengthen Overall Economic Prosperity

The increasing complexity of tax systems and the limitations of traditional rule-based audits have highlighted the need for adaptive, transparent, and efficient auditing solutions. This paper presents the design and evaluation of a Secure AI-Driven Adaptive Audit Transparency Engine (AI-AATE), a novel framework integrating machine learning, explainable AI (XAI), and human-in-the-loop oversight to enhance tax compliance, reduce administrative inefficiencies, and strengthen economic outcomes. The architecture combines supervised and unsupervised models for risk detection, continuous feedback incorporation for adaptive learning, and comprehensive audit logging to ensure transparency, fairness, and traceability. A rigorous evaluation framework employing operational Key Performance Indicators (KPIs), counterfactual simulations, and economic modeling quantifies performance across audit yield, coverage, processing efficiency, revenue recovery, and equity. Governance and trust metrics assess explainability, human oversight, and bias mitigation, linking design principles to measurable institutional outcomes. Simulation results demonstrate that AI-AATE can significantly improve detection of non-compliance, optimize resource allocation, and support equitable and accountable audit selection compared to traditional approaches. By bridging technical design, performance evaluation, and economic impact assessment, this study contributes a holistic methodology for AI-enabled audit systems, offering actionable insights for policymakers, tax authorities, and researchers. The findings underscore the potential of AI-AATE to transform public-sector auditing while maintaining fairness, legitimacy, and public trust, addressing a critical gap in the literature on adaptive, transparent, and secure AI applications in taxation.