Scalable Cloud Native Banking Infrastructure with Deep Neural Networks and SAP Integrated Intelligence

Abstract

The banking industry today confronts rapid disruption: explosive growth in digital transactions, increasingly sophisticated fraud and risk‑scenarios, and a push for personalized customer experiences. To address these demands, this paper proposes an architecture for a scalable cloud‑native banking infrastructure that integrates deep neural networks (DNNs) for intelligence and leverages an enterprise resource‑planning platform from SAP SE. The architecture combines microservices, containers, orchestration, real‑time data pipelines, and AI inference/training workflows. We describe how the system supports large‑scale data ingestion, model training and deployment, and SAP‑integrated business processes for banking operations (e.g., credit scoring, fraud detection, customer segmentation). Empirical experiments demonstrate that a DNN model for credit default prediction deployed on the cloud‑native framework achieved improved throughput and lower latency compared with legacy systems. We also discuss integration challenges (data governance, regulatory compliance, model explainability) and how the SAP layer streamlines business‑process intelligence and orchestration. The results show that combining cloud‑native infrastructure with DNNs and SAP‑integrated intelligence yields superior scalability, agility, and operational responsiveness. However, trade‑offs remain in complexity, cost, and regulatory readiness. We conclude with future directions including hybrid‑cloud architectures, continuous learning pipelines, and stronger model governance frameworks.