AI-Driven Analytical Module for Evaluating and Enhancing Public Spending Efficiency on Corruption Prevention

The evaluation of budgetary expenditure efficiency is a significant methodological and empirical challenge, particularly in domains such as anti-corruption, where public goods are difficult to quantify. Traditional approaches that link the value of public goods to allocated resources often fail to capture the complexity of societal perceptions, which depend not only on actual outcomes but also on communication about them. These limitations hinder the identification of reserves for improving the effectiveness of public spending.Recent research emphasizes the potential of artificial intelligence (AI) to overcome such constraints. Scholars highlight AI’s role in automating administrative processes, enhancing decision-making, ensuring transparency in public finance, and preventing fraud, while also noting institutional and economic contradictions in its implementation. However, empirical applications of AI-driven tools in budget management remain underdeveloped.This study aims to propose an AI-Driven Analytical Module for Evaluating Public Spending Efficiency on Corruption Prevention and to substantiate recommendations for improving evaluation methodologies through the integration of modern digital technologies.