Cloud Native Microservices and IoT Architectures for AI Driven Fraud Detection Predictive Maintenance and Mobile Healthcare Intelligence with SAP
DOI:
https://doi.org/10.15662/IJEETR.2025.0706030Keywords:
Cloud-Native Architecture, Microservices, Internet of Things (IoT), Artificial Intelligence, Fraud Detection, Predictive Maintenance, Mobile Healthcare, SAP BTP, SAP HANA, Enterprise IntelligenceAbstract
The rapid evolution of cloud-native technologies, Internet of Things (IoT), and artificial intelligence (AI) has transformed the design of intelligent enterprise systems. This paper explores how cloud-native microservices architectures integrated with IoT platforms enable scalable, resilient, and real-time AI-driven applications for fraud detection, predictive maintenance, and mobile healthcare intelligence within SAP ecosystems. Traditional monolithic enterprise systems struggle to handle the volume, velocity, and variety of data generated by connected devices and digital transactions. Cloud-native microservices, deployed on container orchestration platforms, provide modularity, elasticity, and fault tolerance, making them suitable for data-intensive AI workloads. IoT architectures facilitate continuous data ingestion from sensors, medical devices, and transactional systems, while AI models perform real-time analytics, anomaly detection, and predictive insights. SAP technologies such as SAP Business Technology Platform (BTP), SAP IoT, SAP HANA, and SAP AI Core enable seamless integration of business processes with advanced analytics and machine learning. This study examines architectural patterns, deployment strategies, and data pipelines supporting AI-driven intelligence across multiple domains. The paper also discusses research challenges, benefits, and limitations associated with security, latency, data governance, and system complexity. The findings highlight the importance of cloud-native and IoT convergence in building intelligent, scalable, and sustainable enterprise AI solutions.
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