Adaptive Digital Twin Architectures for Intelligent Portfolio Risk Management Using Deep Reinforcement Learning

Abstract

The integration of intelligent computational frameworks with financial risk management systems represents a critical frontier in contemporary industrial and technological landscapes. This study investigates the deployment of adaptive digital twin architectures for dynamic portfolio risk prediction, leveraging deep reinforcement learning as a core analytical engine. Digital twins, initially conceptualized within cyber-physical production systems, have evolved to encompass multi-layered, context-aware, and predictive frameworks capable of simulating complex real-world processes with high fidelity (Yun, Park, & Kim, 2017; Garcés et al., 2021). By constructing an intelligent cloud-based framework, financial institutions can simulate, monitor, and optimize investment portfolios, responding dynamically to market volatility, stochastic perturbations, and behavioral anomalies. Mirza et al. (2025) provide foundational evidence of the efficacy of deep reinforcement learning models in this domain, demonstrating significant improvements in predictive accuracy and risk mitigation capabilities.

This research synthesizes theoretical constructs from cyber-physical systems, industrial Internet of Things paradigms, and financial analytics to develop an integrated model that addresses limitations of conventional risk assessment approaches. Through extensive literature review and methodological design, this study elaborates a multi-layered architecture incorporating sensing, data aggregation, predictive modeling, and decision-making modules. Each component is designed to interoperate seamlessly with cloud-based infrastructures, ensuring scalability, real-time responsiveness, and adaptive learning capabilities. The proposed framework not only enhances predictive fidelity but also introduces novel mechanisms for model validation, anomaly detection, and continuous improvement, establishing a paradigm for future research in intelligent risk management systems.

Empirical analysis reveals that deep reinforcement learning algorithms, when embedded within digital twin architectures, exhibit superior performance in identifying latent risk factors, anticipating market shocks, and optimizing asset allocation strategies. By incorporating dynamic feedback loops and self-adaptive learning mechanisms, the framework mitigates the shortcomings of static models, providing a robust and resilient solution for modern financial systems. The implications of this research extend to strategic portfolio management, regulatory compliance, and the broader adoption of intelligent industrial frameworks across the financial sector.