Artificial Intelligence In Public Financial Management: A Systematic Review Of Readiness, Governance, Risks, And Applications In Budgeting, Procurement, And Expenditure Monitoring

Artificial Intelligence (AI) is increasingly transforming public-sector operations by enhancing data-driven decision-making, automation, and governance capabilities. Within Public Financial Management (PFM), AI applications have emerged in budgeting, procurement, auditing, fraud detection, and expenditure monitoring, offering significant opportunities to improve efficiency, transparency, accountability, and fiscal sustainability. Despite growing interest in AI-enabled governance, existing research remains fragmented across technological, organizational, and governance perspectives. This study addresses this gap by conducting a systematic literature review of AI applications in Public Financial Management, with particular emphasis on budgeting, procurement, and expenditure monitoring. Guided by the PRISMA 2020 framework, the review synthesized 72 studies published between 2015 and 2026 from academic journals, conference proceedings, books, and institutional reports. Thematic analysis identified five major themes: (1) AI applications in Public Financial Management, (2) organizational readiness for AI adoption, (3) algorithmic accountability and responsible AI governance, (4) implementation risks and challenges, and (5) Public Financial Management performance outcomes. Findings indicate that successful AI implementation depends not only on technological capability but also on data readiness, digital infrastructure, human capital, leadership commitment, regulatory preparedness, and robust governance mechanisms. Transparency, explainability, accountability, auditability, ethical compliance, and human oversight emerged as critical determinants of responsible AI adoption. Drawing upon the review findings and Toledo’s (2026) algorithmic accountability framework, the study proposes a conceptual governance framework linking AI readiness, AI adoption, governance mechanisms, risk management, and PFM performance. The framework contributes to the literature by integrating previously fragmented research streams and provides policymakers and practitioners with a structured approach for the responsible deployment of AI in government financial systems.