A Secure 5G-Enabled Cloud and AI Computing Model for Financial Risk Prediction and Healthcare Intelligence Using Deep Learning

Abstract

The rapid adoption of 5G networks, cloud computing, and artificial intelligence has enabled the development of intelligent and data-driven applications across financial and healthcare domains. However, the integration of these technologies introduces significant challenges related to security, scalability, latency, and risk management. This paper proposes a secure 5G-enabled cloud and AI computing model that leverages deep learning techniques for financial risk prediction and healthcare intelligence. The proposed architecture integrates cloud-based data processing, 5G-enabled low-latency communication, and deep learning models to analyze large-scale heterogeneous data in real time. Advanced security mechanisms, including encryption, access control, and secure data transmission, are incorporated to protect sensitive financial and medical information. The model enables accurate risk assessment, anomaly detection, and predictive analytics while ensuring data privacy and regulatory compliance. Experimental evaluation demonstrates improved prediction accuracy, reduced response time, and enhanced system reliability compared to traditional cloud-based approaches. The proposed framework offers a scalable and secure solution for next-generation intelligent financial and healthcare systems.