AI-Based Predictive Analytics for Economic and Financial System Risk
Keywords:
Predictive Analytics, Systemic Risk, Financial Infrastructure, Artificial Intelligence, Algorithmic Governance, Sustainability, Socio-Technical Systems.Abstract
The increasing interconnectedness of global financial markets has necessitated a transition from traditional econometric models toward sophisticated, artificial intelligence-driven predictive analytics for systemic risk assessment. This paper provides a comprehensive systems-level analysis of AI-based predictive frameworks designed to identify, quantify, and mitigate risk within economic and financial infrastructures. We explore the architectural trade-offs inherent in large-scale predictive systems, specifically focusing on the tension between model complexity and operational interpretability. The discussion extends into the socio-technical dimensions of AI deployment, addressing the physical requirements of high-performance computing, the necessity of robust data governance, and the environmental sustainability of compute-intensive financial modeling. Furthermore, we examine the policy implications of algorithmic convergence, where the widespread adoption of similar predictive models among systemically important financial institutions may inadvertently synchronize market behaviors and amplify fragility. The research also scrutinizes the ethical imperatives of fairness and equity in capital distribution, arguing that predictive systems must be audited for historical biases to prevent the automated marginalization of specific economic sectors. By synthesizing perspectives from engineering, computational finance, and public policy, this work offers a roadmap for the development of resilient, transparent, and socially responsible risk analytics. We conclude that while AI offers unprecedented capabilities for navigating the uncertainties of the twenty-first-century economy, its success is contingent upon a holistic approach that integrates technical precision with institutional accountability and environmental stewardship.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
