Cybersecure Cloud AI Banking Platform for Financial Forecasting and Analytics in Healthcare Systems

Abstract

Digital banking systems increasingly rely on advanced analytics and artificial intelligence (AI) to enhance decision-making, risk management, customer personalization, and operational efficiency. Financial forecasting — including credit risk assessment, liquidity modeling, fraud detection, and market trend prediction — demands scalable, reliable, and secure AI architectures that can process high-velocity, high-volume data in real time. Traditional on-premises systems struggle to meet these requirements due to limited scalability and inflexible infrastructure, motivating a shift toward cloud-native architectures. This paper proposes a Cloud AI Architecture for Scalable Financial Forecasting and Predictive Analytics tailored for digital banking systems. Leveraging distributed computing, microservices, containerization, and managed AI/ML platform services, the architecture integrates data ingestion layers, feature stores, real-time and batch processing pipelines, model training and deployment workflows, and governance frameworks to ensure compliance, explainability, and operational resiliency. We describe the design principles, key architectural components, and integrated tools necessary to support end-to-end financial forecasting use cases. Experimental evaluation using representative banking workloads demonstrates improved scalability, lower latency, and enhanced forecasting accuracy compared with traditional systems. The findings indicate that a cloud AI architecture provides a strategic advantage for digital banks seeking to transform data into actionable insights while maintaining data governance and regulatory compliance.