Algorithmic Approaches to Enhancing Economic Intelligence through Data Mining

Abstract

The accelerating complexity of global financial systems demands analytical tools capable of distilling actionable insights from vast, heterogeneous economic datasets. This paper presents the Adaptive Algorithmic Economic Intelligence (AAEI) framework, a novel multi-layer data mining architecture that integrates temporal pattern mining, ensemble classification, and an economic signal fusion mechanism to enhance economic intelligence extraction. The proposed system introduces two domain-specific evaluation metrics: the Economic Intelligence Quotient (EIQ) and Temporal Predictive Efficiency (TPE), which together assess the quality and timeliness of mined economic knowledge. Experiments conducted on the Federal Reserve Economic Data Monthly Database (FRED-MD) and the World Bank Economic Complexity Index (WorldBank ECI) demonstrate that AAEI achieves accuracy scores of 91.2% and 89.7%, respectively, outperforming established baselines including XGBoost, LSTM, SVM, and Random Forest by margins of 3.2% to 14.1%. The framework offers a principled pathway for deploying intelligent data mining in macroeconomic forecasting and financial decision support.