AI and Machine Learning–Based Risk Governance Framework for SAP Cloud: A Re-Architected Model for Scalable and Secure Enterprise Systems
DOI:
https://doi.org/10.15662/IJARCST.2024.0705008Keywords:
Cloud ERP, digital payments, SAP HANA, machine learning, real time analytics, microservices, scalable software developmentAbstract
In the evolving digital economy, organisations increasingly demand scalable, cloud‑based enterprise resource planning (ERP) systems capable of processing high‑volume payments with intelligence and agility. This paper presents a framework for intelligent digital payment optimisation within a cloud ERP environment, leveraging the in‑memory platform SAP HANA and embedding machine‑learning techniques. The proposed architecture supports real‑time payment decisioning, anomaly detection, dynamic routing and predictive settlement, all built upon a cloud‑native microservices deployment integrated into the ERP backbone. A pilot implementation is described: payment flow data from a simulated enterprise are ingested into SAP HANA, processed by ML models trained for fraud detection and routing optimisation, and results fed back into the ERP payment module for immediate action and dashboarding. The evaluation demonstrates substantial improvements in processing latency, decision accuracy and scalability compared to a baseline batch‑oriented payment workflow. Further, the system supports continuous model retraining and adapts to changing payment patterns. The paper discusses key design considerations (data architecture, model lifecycle, integration with ERP modules), highlights advantages (real‑time insight, scalability, intelligence) and disadvantages (complexity, cost, vendor‑lock‑in, risk of model drift) of the approach. Finally, it outlines future research directions including cross‑enterprise payment orchestration, multi‑cloud resilience, and autonomous payment decisioning.
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