A Cybersecurity-First Deep Learning Architecture for Healthcare Cost Optimization and Real-Time Predictive Analytics in SAP-Based Digital Banking Systems

Abstract

The rapid digitalization of healthcare payments and financial workflows has intensified the need for secure, intelligent, and cost-efficient digital banking infrastructures. Healthcare organizations increasingly rely on SAP-based digital banking systems to manage claims processing, billing, and financial transactions, yet these systems face challenges related to escalating operational costs, cyber threats, and limited real-time intelligence. This paper proposes a cybersecurity-first deep learning architecture designed to optimize healthcare costs while enabling real-time predictive analytics within SAP-based digital banking environments. The proposed framework integrates deep learning models for cost prediction, anomaly detection, and demand forecasting with secure data pipelines, encryption, identity and access management, and continuous threat monitoring. Real-time analytics are achieved through event-driven processing and SAP-native services, allowing proactive decision-making and early risk mitigation. By unifying cybersecurity controls with advanced AI-driven analytics, the architecture enhances financial transparency, reduces fraud and inefficiencies, and supports scalable, compliant healthcare financial operations. The proposed approach demonstrates the potential to significantly improve cost optimization, security posture, and operational resilience in next-generation healthcare-focused digital banking systems.