Next-Generation SAP Cloud Re-Architecture: AI-Driven Risk Detection and Security Optimization with Real-Time Neural Network Intelligence
Main Article Content
Abstract
The rise of digital payments, distributed payment channels, and real‑time settlement has made the payment ecosystem a critical component of modern enterprise operations. In parallel, cloud‑based enterprise resource planning (ERP) platforms built on in‑memory databases such as SAP S/4HANA/SAP HANA are evolving into intelligent platforms. This paper explores a framework for embedding artificial intelligence (AI) and machine learning (ML) into a cloud ERP environment to deliver a smart digital payment ecosystem. The proposed architecture supports real‑time transaction ingestion, adaptive payment routing, anomaly and fraud detection, dynamic payment cost optimisation, and cash‑flow forecasting. We discuss the integration of ML pipelines with SAP HANA’s in‑memory capabilities, the challenges of scale in a high‑volume payment landscape, and deployment considerations for multi‑tenant cloud ERP infrastructures. A proof‑of‑concept simulation demonstrates improved detection performance, reduced payment latency and better payment‑operation visibility. The findings indicate that embedding AI/ML within cloud ERP platforms enables enterprises to transform payment operations from a cost‑centre into a strategic intelligence asset, while highlighting practical trade‑offs in complexity, governance and model lifecycle.
Article Details
Section
How to Cite
References
1. Abdelilah Khaled & Mohammed Abdou Janati Idrissi (2012). A Semi Structured Tailoring Driven Approach for ERP Selection. arXiv. arxiv.org
2. R., Sugumar (2023). Real-time Migration Risk Analysis Model for Improved Immigrant Development Using Psychological Factors. Migration Letters 20 (4):33-42.
3. HV, M. S., & Kumar, S. S. (2024). Fusion Based Depression Detection through Artificial Intelligence using Electroencephalogram (EEG). Fusion: Practice & Applications, 14(2).
4. Pasumarthi, A. (2022). Architecting Resilient SAP Hana Systems: A Framework for Implementation, Performance Optimization, and Lifecycle Maintenance. International Journal of Research and Applied Innovations, 5(6), 7994-8003.
5. Adari, V. K. (2024). How Cloud Computing is Facilitating Interoperability in Banking and Finance. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(6), 11465-11471.
6. Sridhar Kakulavaram. (2022). Life Insurance Customer Prediction and Sustainbility Analysis Using Machine Learning Techniques. International Journal of Intelligent Systems and Applications in Engineering, 10(3s), 390 –.Retrieved from https://ijisae.org/index.php/IJISAE/article/view/7649
7. Poornima, G., & Anand, L. (2024, May). Novel AI Multimodal Approach for Combating Against Pulmonary Carcinoma. In 2024 5th International Conference for Emerging Technology (INCET) (pp. 1-6). IEEE.
8. Kesavan, E. (2024). Big Data Analytics: Tools, Technologies, and Real-World Applications–A Review. International Journal of Innovations in Science, Engineering And Management, 120-126.https://ijisem.com/journal/index.php/ijisem/article/view/315/280 Sreenivasa Rao Sola (2020). ERP Cloud and Procurement: Unlocking New Levels of Automation and Integration. International Journal of Leading Research Publication (IJLRP), 1(1). ijlrp.com
9. AKTER, S., ISLAM, M., FERDOUS, J., HASSAN, M. M., & JABED, M. M. I. (2023). Synergizing Theoretical Foundations and Intelligent Systems: A Unified Approach Through Machine Learning and Artificial Intelligence.
10. Idris Opeyemi Lawal (2023). Next generation ERP systems: leveraging AI and machine learning for intelligent process automation. International Journal of Core Engineering & Management, 7(08). ijcem.in
11. Manoj Gudala (2022). ERP Integration for Business Excellence: Leveraging Cloud AI and Mobile Technologies in the Digital Landscape. Journal of Artificial Intelligence & Cloud Computing. onlinescientificresearch.com
12. Kohli, M. (2018). Supplier Evaluation Model on SAP ERP Application using Machine Learning Algorithms. International Journal of Engineering and Technology, 7(2.28), 306–311. sciencepubco.com
13. Rahanuma, T., Md Manarat Uddin, M., & Sakhawat Hussain, T. (2023). Safeguarding Vulnerable Care Access: AI-Powered Risk Detection and Microfinance Linking for Community Health Small Businesses. American Journal of Engineering, Mechanics and Architecture, 1(4), 31-57.
14. Manda, P. (2023). Migrating Oracle Databases to the Cloud: Best Practices for Performance, Uptime, and Risk Mitigation. International Journal of Humanities and Information Technology, 5(02), 1-7.
15. Journal of Big Data. (2022). A machine learning based credit card fraud detection using the GA algorithm for feature selection. Journal of Big Data, 9(24). https://doi.org/10.1186/s40537 022 00573 8
16. Sivaraju, P. S. (2023). Global Network Migrations & IPv4 Externalization: Balancing Scalability, Security, and Risk in Large-Scale Deployments. ISCSITR-INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS (ISCSITR-IJCA), 4(1), 7-34.
17. Kumar, R., Christadoss, J., & Soni, V. K. (2024). Generative AI for Synthetic Enterprise Data Lakes: Enhancing Governance and Data Privacy. Journal of Artificial Intelligence General science (JAIGS) ISSN: 3006-4023, 7(01), 351-366.
18. Kotapati, V. B. R., Perumalsamy, J., & Yakkanti, B. (2022). Risk-Adapted Investment Strategies using Quantum-enhanced Machine Learning Models. American Journal of Autonomous Systems and Robotics Engineering, 2, 279-312.
19. Venkata Ramana Reddy Bussu. “Databricks- Data Intelligence Platform for Advanced Data Architecture.” Volume. 9 Issue.4, April - 2024 International Journal of Innovative Science and Research Technology (IJISRT), www.ijisrt.com, ISSN - 2456-2165, PP :-108-112:-https://doi.org/10.38124/ijisrt/IJISRT24APR166
20. Binu, C. T., Kumar, S. S., Rubini, P., & Sudhakar, K. (2024). Enhancing Cloud Security through Machine Learning-Based Threat Prevention and Monitoring: The Development and Evaluation of the PBPM Framework. https://www.researchgate.net/profile/Binu-C-T/publication/383037713_Enhancing_Cloud_Security_through_Machine_Learning-Based_Threat_Prevention_and_Monitoring_The_Development_and_Evaluation_of_the_PBPM_Framework/links/66b99cfb299c327096c1774a/Enhancing-Cloud-Security-through-Machine-Learning-Based-Threat-Prevention-and-Monitoring-The-Development-and-Evaluation-of-the-PBPM-Framework.pdf
21. Poornima, G., & Anand, L. (2024, April). Effective Machine Learning Methods for the Detection of Pulmonary Carcinoma. In 2024 Ninth International Conference on Science Technology Engineering and Mathematics (ICONSTEM) (pp. 1-7). IEEE.
22. Amuda, K. K., Kumbum, P. K., Adari, V. K., Chunduru, V. K., & Gonepally, S. (2024). Evaluation of crime rate prediction using machine learning and deep learning for GRA method. Data Analytics and Artificial Intelligence, 4 (3).
23. Peddamukkula, P. K. How Technology is Making Life Insurance Smarter and Faster: The Role of Cloud and Automation. https://www.researchgate.net/profile/Praveen-Peddamukkula/publication/397017728_How_Technology_is_Making_Life_Insurance_Smarter_and_Faster_The_Role_of_Cloud_and_Automation/links/69023a0cc900be105cbd89d5/How-Technology-is-Making-Life-Insurance-Smarter-and-Faster-The-Role-of-Cloud-and-Automation.pdf
24. Kandula, N. Machine Learning Approaches to Predict Tensile Strength in Nanocomposite Materials a Comparative Analysis.
25. Ramanathan, U.; Rajendran, S. Weighted Particle Swarm Optimization Algorithms and Power Management Strategies for Grid Hybrid Energy Systems. Eng. Proc. 2023, 59, 123. [Google Scholar] [CrossRef]
26. Deng, R., & Ruan, N. (2019). FraudJudger: Real-world data oriented fraud detection on digital payment platforms. arXiv. https://arxiv.org/abs/1909.02398 ournal of Big Data. (2022). A machine-learning based credit card fraud detection using the GA algorithm for feature selection. Journal of Big Data, 9(24). https://doi.org/10.1186/s40537-022-00573-8