Artificial Intelligence for Public Expenditure Analysis in Low-Income Countries: Opportunities and Challenges

Auteur(s): Nzazi Ngabila Boaz, Biaba Kuya Jirince, Ibsen Bazie Giovanie, Tasho Tashev , Witesyavwirwa Kambale , Vinh Ho Tuong, Kyandoghere Kyamakya , Kasoro Mulenda Nathanael, Kasereka Selain
Nom de la revue/Journal: Procedia Computer Science
Mois: novembre
Volume: Volume 272, 2025,
Numéro: https://doi.org/10.1016/j.procs.2025.10.204
Année: 2025
pages: 261-268

Résumé

<p style="text-align:justify;"><span style="color:rgb(31,31,31);">Artificial intelligence (AI) has increasingly become a pivotal instrument in reshaping public financial management across the globe. Nevertheless, its integration within low-income countries (LICs) remains sporadic and underdeveloped. This study conducts a short review of recent academic literature to explore how AI is being utilized in the analysis of public expenditure, placing particular emphasis on LICs. Through a comparative assessment of recent advancements and applied research, the study investigates the primary objectives, methodological frameworks, outcomes, challenges, and limitations associated with AI-driven approaches in areas such as budget prediction, anomaly identification, financial auditing, and expenditure efficiency. The results suggest that AI possesses considerable potential to improve transparency, operational efficiency, and fiscal accountability in the public sector. However, numerous challenges continue to hinder its full deployment, including technological limitations, institutional barriers, and infrastructural deficits. The paper highlights some of the most promising AI methodologies, including machine learning, natural language processing, and robotic process automation, while also pinpointing key implementation and validation gaps. This work contributes to a clearer understanding of the current landscape of AI-driven public expenditure analysis in low-income countries, identifies gaps in existing approaches, and outlines strategic pathways for future research in data-driven fiscal governance.</span></p>