Predictive Analytics for National Budgeting and Expenditure: Leveraging AI/ML on the PFMS 2.0 Data Ecosystem

Abstract

The growing scale, complexity, and speed of the public financial transactions have revealed the shortcomings of the traditional descriptive and diagnostic methodologies of national budgeting and expenditure management. As integrated digital public finance platforms become more mature, more of the opportunity to shift an emphasis on retrospective financial analysis to forward-looking, predictive decision-making is becoming possible. This paper will discuss the application of artificial intelligence (AI) and machine learning (ML) approaches to the PFMS 2.0 data ecosystem that could improve the national budgeting, expenditure forecasting, and fiscal policy simulation process. Based on the longitudinal, granular, and multi-dimensional structure of PFMS 2.0 data, the research suggests hierarchical frameworks of predictive public finance to integrate the time-series forecasting, machine learning-based utilization modeling, and policy scenario analysis. The framework focuses on using enriched datasets which include the macroeconomic indicators to enhance the accuracy of the forecasting and the responsiveness of the fiscal policy. Moreover, the idea of having a digital twin of national budgetary systems is introduced, which allows policy makers to model the results of expenditures and determine a trade-off in changing economic and policy conditions. Other important governance issues that the article raises with regards to AI-driven public finance are algorithmic bias, transparency, and accountability. The suggested solution will help the attainment of credible, auditable, and policy-conformant decision-making by implementing predictive financial systems which have explainable AI (XAI) mechanisms and ethical controls. On the whole, this article makes predictive analytics a core competency of contemporary public financial management, which allows conducting the activities of a national treasury more flexibly, efficiently, and evidence-based.