Financial Risk Forecasting Using AI: Hybrid Econometric–Machine Learning Approaches

Abstract

The increasing complexity of global financial systems necessitates more accurate and adaptive methods for predicting financial risks. This study investigates hybrid Econometric–Machine Learning Approaches, integrating traditional econometric models with advanced artificial intelligence (AI) techniques to enhance predictive accuracy and robustness. Employing hybrid models such as ARIMA–LSTM, GARCH–XGBoost, and VAR–Random Forest, the research analyzes historical financial datasets, including stock indices, exchange rates, and macroeconomic indicators. The hybrid frameworks outperform standalone econometric and machine learning models in forecasting market volatility and risk exposure, achieving higher precision in identifying non-linear dependencies and dynamic interactions. Results reveal that AI-enhanced econometric methods capture both short-term fluctuations and long-term structural trends, offering a comprehensive risk assessment mechanism for financial institutions. The study concludes that hybrid AI models significantly improve the early detection of financial instability, allowing policymakers, investors, and analysts to make more informed and timely decisions. Future research should explore real-time financial risk prediction using adaptive learning algorithms and cross-market data integration to strengthen resilience in volatile economic conditions.