Intelligent Healthcare and Banking ERP on SAP HANA with Real-Time ML Fraud Detection
DOI:
https://doi.org/10.15662/IJARCST.2024.0701801Keywords:
Intelligent ERP, SAP HANA, Machine Learning, Real-Time Fraud Detection, Healthcare Systems, Banking Integration, Anomaly DetectionAbstract
This paper presents an Intelligent Healthcare and Banking ERP system developed on the SAP HANA platform, integrating advanced machine learning techniques to achieve real-time fraud detection and sector-wide operational intelligence. The unified ERP architecture connects healthcare management functions with banking and financial workflows, enabling seamless interoperability, consistent data flow, and centralized monitoring. Leveraging SAP HANA’s in-memory processing capabilities, the system ensures ultra-fast data computation, instant access to patient and financial records, and accelerated decision-making across departments. Machine learning models embedded within the ERP continuously analyze transactional behaviors, identify unusual patterns, and detect potential credit card or financial fraud with high accuracy. The system also incorporates automated alert mechanisms, predictive analytics dashboards, and dynamic risk scoring to ensure proactive threat mitigation. In addition, the integration of adaptive cybersecurity controls enhances data privacy, regulatory compliance, and resilience against emerging digital threats. By merging healthcare information systems, enterprise financial operations, and intelligent fraud analytics into one ecosystem, the proposed solution delivers a scalable, secure, and future-ready ERP platform. This holistic approach supports digital transformation initiatives, strengthens organizational efficiency, and builds user trust in both healthcare and banking domains.
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