Public Debt Management With Advanced Data Analytics
Year: 2024
Type: article
Abstract: Public debt management is a cornerstone of economic stability, yet traditional methods often struggle to address the complexities of contemporary economic environments characterized by volatility, interconnected markets, and rapid technological advancements. This research explores the transformative role of advanced data analytics in modernizing public debt management practices. By integrating predictive analytics, risk assessment tools, and portfolio optimization techniques, advanced analytics offers innovative solutions to enhance forecasting accuracy, manage risks proactively, and optimize debt portfolios. Through an analysis of case studies from both developing and developed economies, the study highlights the global applicability and scalability of these tools. Key findings reveal that advanced analytics significantly improve fiscal resilience, reduces borrowing costs, and fosters long-term sustainability. However, challenges such as the need for robust data infrastructure, skilled personnel, and high initial investments remain barriers to widespread adoption. This paper underscores the critical need for continued research, innovation, and collaboration to fully leverage the potential of data analytics in public debt management, ensuring sustainable economic growth in an increasingly dynamic global landscape.
Source: AI and Data Science Journal
DOI: 10.61784/adsj3001
Author: Rakibul Hasan Chowdhury
Institutions: Trine University, University of Portsmouth
Language: English
Subfield: Economics and Econometrics
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W4405764819