AI-Driven Early Warning System for Financial Risk in the U.S. Digital Economy

The digital economy’s increasing integration with financial systems has amplified systemic vulnerabilities, yet most early warning frameworks remain static and slow to adapt to evolving market, macroeconomic, and sentiment dynamics. This study develops an AI-driven early warning system that fuses macroeconomic indicators, market data from major digital firms, and online sentiment metrics to detect and anticipate financial stress within the U.S. digital economy. Leveraging a decade of weekly data (2015–2025) across FRED, Yahoo Finance, Reddit, and Google News sources, we construct a multimodal dataset capturing both structural and behavioral signals. A hybrid modeling pipeline integrates interpretable Logistic Regression with adaptive online learning using the River framework and ADWIN drift detection, enabling real-time updates as financial conditions evolve. Experiments benchmark Logistic Regression, Random Forest, and XGBoost against the adaptive model under temporal cross-validation. Results show that the interpretable logistic regression baseline achieves the most stable performance across folds, while the adaptive River model sustains predictive capability under concept drift, providing continuous calibration without retraining. Lead-time analysis demonstrates the system’s ability to issue warnings 2–6 weeks before major stress events, validated through both macroeconomic and sentiment-driven market shifts. Explainability analysis using SHAP reveals that negative sentiment dynamics, rising volatility, and tightening monetary conditions are the strongest precursors of stress. These findings highlight how transparent, adaptive AI systems can bridge the gap between accuracy, interpretability, and operational feasibility, offering a practical foundation for real-time systemic risk monitoring in digital financial ecosystems.