Digital Twin–Driven Reinforcement Learning for Proactive Cyber-Financial Threat Mitigation in Smart Healthcare Systems

The increasing integration of digital healthcare infrastructures with financial systems has significantly expanded the cyber-attack surface, leading to complex threats that simultaneously compromise patient safety, data privacy, and financial stability. Traditional reactive cybersecurity mechanisms are insufficient to address the dynamic and interconnected nature of cyber-financial risks in smart healthcare environments. This paper proposes a Digital Twin–driven Deep Reinforcement Learning (DT-DRL) framework for proactive cyber-financial threat mitigation in smart healthcare systems. The proposed approach constructs a virtual digital twin of healthcare cyber-physical and financial ecosystems, incorporating electronic health records, Internet of Medical Things (IoMT) devices, billing platforms, and network security components. A deep reinforcement learning agent is trained within the digital twin to learn optimal defense policies that minimize financial losses, service disruption, and security breaches under diverse attack scenarios. The framework models cyber incidents such as ransomware, insider threats, and billing fraud while explicitly quantifying their financial impact. Experimental results demonstrate that the DT-DRL approach outperforms conventional static and rule-based security strategies in terms of threat response time, risk reduction, and cost efficiency. The proposed system provides a scalable and intelligent decision-support solution for enhancing cybersecurity resilience and financial risk management in next-generation smart healthcare infrastructures.