AI-Driven Integrated Framework for Real-Time Fiscal Impact Analysis in Government Financial Management

Abstract

A real-time fiscal impact analysis capability would improve the accuracy and transparency of government financial management and enable quicker, evidence-informed decisions in response to dynamic economic conditions. Such capability supports the automatic generation of models and assumptions required to assess the fiscal consequences of policy proposals, anticipated revenue collection, or spending out-turns as well as delivering counterfactual analyses that compare likely trajectories under different scenarios. A theoretically sound architecture defines the supporting integrated data-engineering and computing framework together with a set of analytic methods that encompass the use of economic models, parameterization, scenario generation, counterfactual and policy simulation techniques, and uncertainty quantification.

Governments face an oversight challenge—ensuring that sufficient detail and scrutiny are applied to policies likely to have large fiscal consequences while at the same time being able to act quickly when economic conditions require a timely response. Such crises can demand changes to existing policies or the introduction of new measures. The desire for a more rapid assessment of government policies and a demand for more transparency have rekindled a conversation within governments about improving the quality of fiscal transparency and providing real-time advice on the potential future impacts of policies and decisions.