Cloud-Native Twin Systems for Real-Time Risk and Compliance Simulation in FinHealth Converged Ecosystems

Abstract

The paper fulfills such urgent and timely requirements as real-time risk management and regulatory compliance on FinHealth converged ecosystems through the implementation of a cloud-native twin system architecture. The traditional systems also lack capacity to deal with the complexity, high velocity financial and healthcare data with acceptable transparency, latency and scalability, thus resulting in slow responses to compliance issues and inability to detect the violation. In the current paper, I suggest a hybrid AI-digital twin model running on the microservices platform that follows a Kubernetes-based structure to model the regulatory events and apply policy enforcement due to the alignment of policies dynamically.

The empirical accessibility testing done on a number of 1.8 million FinHealth transactions showed high positivity: up to 42.3 percent decrease in latency (reduced to 640 ms to 1110 ms), the SLA remained elevated by over 94.3 percent during heavy traffic, and manual interventions were reduced by more than 47 percent in high-risk cases of violation. LSTM Twin model yielded a better result compared to stand alone AI whereby the accuracy was 90.2 percent, F1-score and recall 86.4 percent and 85.3 percent respectively. The quadrant-based prioritization and the prediction of risks into violation were carried out by real time simulations, which accelerated the remediation procedures by 37 percent.

The results demonstrate the feasibility and practicability of using digital twins as a process to transform compliance checkups of FinHealth platforms. The proposed framework addresses the imbalance between control and response and the foundation of active and cloud-native-compatible compliance frameworks at cloud-native architecture.