Predictive Analytics for Trade Policy Optimization: A Data-Driven Approach to Economic Forecasting and Decision-Making
DOI:
https://doi.org/10.26438/ijcse/v13i8.4248Keywords:
Policy Optimization,, Predictive Analytics,, Trade Policy,, Trade Flow Analysis, Machine LearningAbstract
Global trade complexity requires data-driven approaches for resilient policy design. This study presents a predictive analytics framework integrating demographic, socio-economic, geopolitical, and real-time trade data with machine learning, econometric models, and reinforcement learning. The framework enhances forecasting accuracy by 25–30% compared to traditional methods and enables policymakers to balance growth, stability, and liberalization objectives. Case studies on tariff reductions, export incentives, and quota removals demonstrate improved competitiveness and economic forecasting. Results show that AI-driven predictive analytics strengthens resilience against external shocks while fostering evidence-based, transparent, and adaptive trade policy, advancing sustainable and inclusive global economic development.
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