Quantum-Inspired Optimization and Explainable AI for Dynamic Downside Risk Monitoring in Financial Time-Series Systems
Keywords:
quantum-inspired optimization, explainable artificial intelligence, downside risk, financial time-series, dynamic monitoring, system architecture, governance, sustainabilityAbstract
The increasing complexity and interconnectivity of global financial markets demand robust, interpretable, and adaptive frameworks for monitoring downside risk. Traditional risk models, such as value-at-risk and expected shortfall, often rely on static assumptions and fail to capture nonlinear dependencies and regime shifts in high-frequency time-series data. This paper proposes a hybrid system architecture that integrates quantum-inspired optimization techniques with explainable artificial intelligence to dynamically monitor downside risk in financial time-series systems. The quantum-inspired component leverages metaheuristic algorithms, including simulated annealing and variational quantum approaches, to solve high-dimensional portfolio optimization and stress-scenario selection problems under realistic constraints. The explainable AI module employs SHAP, LIME, and attention-based mechanisms to provide transparent, auditable justifications for risk signals and model decisions. We examine structural trade-offs between computational efficiency, accuracy, and interpretability, and discuss the governance, deployment, and sustainability implications of such socio-technical infrastructures. The analysis highlights the need for leakage-safe evaluation protocols, regulatory alignment, and fairness-aware design to ensure credible early warning systems. By bridging quantum-inspired computation and interpretable machine learning, the proposed framework offers a pathway toward more resilient and accountable financial risk monitoring.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
