AI-Driven SAP HANA Cloud Platform Enabling Scalable Data Migration and Healthcare Big Data Analytics with Real-Time Fraud Detection
DOI:
https://doi.org/10.15680/IJCTECE.2025.0804010Keywords:
SAP HANA Cloud, data center migration, healthcare big data, machine learning, fraud detection, AI-driven analytics, data quality engineering, real-time processing, in-memory computing, cloud transformation, predictive analytics, healthcare IT modernizationAbstract
Healthcare enterprises increasingly rely on high-performance cloud platforms to handle massive data workloads, ensure regulatory compliance, prevent fraud, and support clinical decision-making. Traditional on-premises systems are strained by rapid data growth, complex interoperability needs, and computational demands of advanced machine learning (ML). This research proposes an AI-driven SAP HANA Cloud Platform designed to streamline scalable data center migration, improve healthcare big data quality, and enable real-time ML-powered fraud detection. By leveraging in-memory processing, multi-model data architecture, AI integration services, and cloud elasticity, SAP HANA Cloud offers a unified solution to modern healthcare challenges. The proposed framework focuses on four pillars: (1) seamless and secure data center migration, (2) healthcare big data governance and quality engineering, (3) ML-powered real-time analytics, and (4) anomaly- and pattern-based fraud detection. Simulated experiments and architectural evaluations demonstrate significant improvements in data latency, analytics performance, data accuracy, and fraud detection precision. The study confirms that an AI-driven SAP HANA Cloud infrastructure can serve as a strategic enabler for healthcare organizations seeking digital transformation, operational efficiency, and robust security. The research concludes by presenting best practices, measurable improvements, and an enterprise blueprint for large-scale cloud modernization.References
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