Intelligent Financial Governance: The Role of AI and Machine Learning in Enhancing Fiscal Impact Analysis and Budget Forecasting for Government Entities

Abstract

Intelligent Financial Governance (IFG) aims at understanding the complex systems underlying accounting, budgeting, and financial data in government entities with the support of computational tools. The goal is to assess how to support decision-makers in managing uncertainties related to financial systems. As financial governance is much a forecast-based question, AI and Machine Learning (ML) technology can be used to build predictive models, which are assessed with respect to different historical and real-time variables. A variety of methodologies are developed and discussed in relation to the reflexive loop needed to appraise the impacts on the insights obtained. Case studies refer to the use of these methodologies in two government entities in Northern Italy, which are commented on to identify implications and potential directions. The essay’s conclusion reflects on the potential role of AI tools in enhancing financial governance in government entities to inform decisions, budget policies, and assessments relating to the complexity of accounting and financial systems. The role of AI is typically not recurring here, either as black-box models or as wide prediction-based systems, which are often difficult to train as there is no universal forecasting pattern in financial systems and variables change at fine timescales.