Scalable AI Framework for SAP Cloud Re-Architecture: Real-Time Risk Detection Using Machine Learning and Artificial Neural Networks
DOI:
https://doi.org/10.15662/IJRAI.2024.0706015Keywords:
cognitive ERP, digital payments security, SAP HANA, cloud integration, artificial intelligence, machine learning, fraud detection, enterprise payment systemsAbstract
This paper proposes a cognitive enterprise‑resource‑planning (ERP) framework aimed at securing digital payments by integrating cloud‑deployable AI components, the in‑memory database platform SAP HANA, and machine learning models within an ERP environment. The proposed architecture leverages cognitive computing techniques—such as anomaly detection, behavioural profiling and adaptive fraud‑analytics—to complement transaction processing flows in digital‑payment modules. Cloud integration ensures scalability and agility while the SAP HANA backend provides real‑time data processing and analytics. Machine learning models (supervised and unsupervised) are embedded to detect fraudulent or anomalous behaviours within payment transactions and support secure authorisation, reconciliation and audit. The paper outlines how such a framework can be implemented, discusses its advantages (real‑time detection, reduced fraud losses, improved user experience) and disadvantages (complexity, cost, governance challenges), and presents a hypothetical implementation scenario with outcomes, followed by discussion. The research concludes that adopting a cognitive ERP framework significantly enhances security in digital payment ecosystems, and identifies future work in deployment across multi‑cloud, cross‑border and blockchain‑enabled payment networks.
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