AI-Augmented Quantitative Finance: A Multi-Methodological Framework Combining Machine Learning, Deep Learning, and Quantum Computing for Predictive Risk Modeling

Abstract: The increasing complexity and volatility of global financial markets highlight the limitations of traditional risk modeling frameworks, which often rely on linear assumptions and fail to capture nonlinear dependencies and systemic shocks. This study proposes an augmented multi-methodological framework that integrates machine learning, deep learning, and quantum computing to advance predictive risk modeling in quantitative finance. Conceptually, the framework builds upon existing AI-based approaches while introducing quantum-enhanced algorithms to address computational bottlenecks in optimization and simulation. The research problem is defined by the absence of a comprehensive, hybrid framework capable of balancing predictive accuracy, scalability, and interpretability in high-dimensional financial environments. The methodology combines machine learning for feature engineering, deep learning for modeling nonlinear temporal dynamics, and quantum computing for accelerating high-dimensional optimization tasks. Empirical testing against structured and unstructured financial datasets demonstrates that the augmented framework significantly outperforms classical econometric models and standalone AI approaches in predictive accuracy and robustness, particularly under crisis conditions. The results further indicate reductions in computational time through quantum-enhanced optimization, without sacrificing model interpretability. The study contributes to theory by extending the scope of quantitative finance into hybrid computational paradigms, and to practice by offering actionable insights for financial institutions, regulators, and policymakers. The implications suggest that multi-methodological convergence represents a critical pathway toward resilient and adaptive financial systems in the digital era. Keywords Augmented quantitative finance, predictive risk modeling, machine learning, deep learning, quantum computing, systemic risk, financial forecasting, portfolio optimization, computational finance, hybrid modeling frameworks