Real-Time Cloud Re-Architecture for SAP: A Machine Learning and Neural Network Framework for Risk Detection and Enterprise Scalability
DOI:
https://doi.org/10.15662/IJEETR.2024.0605004Keywords:
Cloud native ERP, SAP HANA, digital payment processing, real‐time analytics, machine learning, event driven microservices, intelligent paymentsAbstract
Cloud‑native enterprise resource planning (ERP) architectures are increasingly central to organisations seeking to process digital payments intelligently, at scale and in real time. This paper proposes a cloud‑native ERP architecture framework built on the in‑memory platform SAP HANA and augmented with machine‑learning capabilities for real‐time analytics of payment flows. We describe how modern microservices, event‐driven processing, multi‑tenant deployment and elastic cloud storage combine to enable a robust digital payment module within the ERP. We further show how embedding machine learning—for fraud detection, payment anomaly identification, dynamic routing and predictive settlement—supports business responsiveness and intelligence. Advantages of real‐time analytics on transactional data, agility of cloud native deployment and seamless integration into ERP processes (order‑to‑cash, procure‑to‑pay, reconciliation) are discussed. The paper outlines a prototypical methodology, reviews relevant literature, presents a conceptual implementation and discusses results of a pilot. We identify key benefits (reduced latency, improved insight, scalability) and disadvantages (complexity, vendor‑lock‑in, security/ compliance risks) of this approach. We conclude with directions for future work including deeper AI/ML integration, multi‑cloud resilience, and real‑time cross‑enterprise payment orchestration.
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