Design of a Secure SAP-Enabled Cloud Lakehouse for AI-Driven Financial Risk and Healthcare Analytics
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Abstract
The rapid convergence of financial services and healthcare systems has introduced complex challenges related to data security, regulatory compliance, scalability, and intelligent analytics. This paper presents the design of a secure SAP-enabled cloud lakehouse architecture that supports AI-driven financial risk assessment and healthcare analytics using heterogeneous, high-volume data sources. The proposed framework integrates structured and unstructured data from enterprise systems, Internet of Things (IoT) devices, and digital platforms into a unified lakehouse model built on secure cloud infrastructure. Advanced machine learning techniques are employed to enable predictive financial risk modeling, anomaly detection, and real-time healthcare insights, while SAP technologies facilitate enterprise-grade data governance, interoperability, and transactional consistency. Security is enforced through a zero-trust architecture incorporating encryption, identity-based access control, and continuous monitoring to ensure compliance with financial and healthcare regulations. Experimental evaluation demonstrates improved analytical performance, reduced data latency, and enhanced decision accuracy compared to traditional data warehouse approaches. The proposed architecture provides a scalable and secure foundation for intelligent enterprise analytics across regulated domains.
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1. Davenport, T., & Kalakota, R. (2019). The potential for AI in business. MIT Sloan Management Review, 60(4), 22–27.
2. Müller, R., & Klein, G. (2020). Enterprise adoption of SAP S/4HANA: Challenges and opportunities. Journal of Enterprise Information Management, 33(4), 567–588. https://doi.org/10.1108/JEIM-01-2019-0015
3. Patel, S., Kim, H., & Lee, J. (2019). AI-driven network performance optimization in cloud environments. IEEE Transactions on Network and Service Management, 16(3), 1025–1038. https://doi.org/10.1109/TNSM.2019.2912345
4. Zhou, Y., & Deng, X. (2021). Artificial intelligence for cloud security: Emerging techniques and applications. Journal of Cloud Computing, 10(1), 45–67. https://doi.org/10.1186/s13677-021-00234-6
5. Archana, R., & Anand, L. (2025). Residual u-net with Self-Attention based deep convolutional adaptive capsule network for liver cancer segmentation and classification. Biomedical Signal Processing and Control, 105, 107665.
6. Gopinathan, V. R. (2024). Meta-Learning–Driven Intrusion Detection for Zero-Day Attack Adaptation in Cloud-Native Networks. International Journal of Humanities and Information Technology, 6(01), 19-35.
7. Cherukuri BR. Advanced Multi Class Cyber Security Attack Classification in IoT Based Wireless Sensor Networks Using Context Aware Depthwise Separable Convolutional Neural Network. Journal of Machine and Computing. 2025;5(2). https://doi.org/https://anapub.co.ke/journals/jmc/jmc_pdf/2025/jmc_volume_5-issue_2/JMC202505064.pdf
8. Thumala, S. R., & Pillai, B. S. (2024). Cloud Cost Optimization Methodologies for Cloud Migrations. International Journal of Intelligent Systems and Applications in Engineering.
9. Sudhan, S. K. H. H., & Kumar, S. S. (2015). An innovative proposal for secure cloud authentication using encrypted biometric authentication scheme. Indian journal of science and technology, 8(35), 1-5.
10. Madabathula, L. (2024). Reusable streaming pipeline frameworks for enterprise lakehouse analytics. International Journal of Engineering & Extended Technologies Research (IJEETR), 6(4), 8444–8451. https://doi.org/10.15662/IJEETR.2024.0604007
11. Ramakrishna, S. (2024). Intelligent Healthcare and Banking ERP on SAP HANA with Real-Time ML Fraud Detection. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 7(Special Issue 1), 1-7.
12. Kasireddy, J. R. (2022). From raw trades to audit-ready insights: Designing regulator-grade market surveillance pipelines. International Journal of Engineering & Extended Technologies Research (IJEETR), 4(2), 4609–4616. https://doi.org/10.15662/IJEETR.2022.0402003
13. Jayaraman, S., Rajendran, S., & P, S. P. (2019). Fuzzy c-means clustering and elliptic curve cryptography using privacy preserving in cloud. International Journal of Business Intelligence and Data Mining, 15(3), 273-287.
14. Md Manarat Uddin, M., Sakhawat Hussain, T., & Rahanuma, T. (2025). Developing AI-Powered Credit Scoring Models Leveraging Alternative Data for Financially Underserved US Small Businesses. International Journal of Informatics and Data Science Research, 2(10), 58-86.
15. Natta, P. K. (2023). Robust supply chain systems in cloud-distributed environments: Design patterns and insights. International Journal of Research and Applied Innovations (IJRAI), 6(4), 9222–9231. https://doi.org/10.15662/IJRAI.2023.0604006
16. Kusumba, S. (2024). Accelerating AI and Data Strategy Transformation: Integrating Systems, Simplifying Financial Operations Integrating Company Systems to Accelerate Data Flow and Facilitate Real-Time Decision-Making. The Eastasouth Journal of Information System and Computer Science, 2(02), 189-208.
17. Chivukula, V. (2021). Impact of Bias in Incrementality Measurement Created on Account of Competing Ads in Auction Based Digital Ad Delivery Platforms. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 4(1), 4345–4350.
18. Thambireddy, S. (2022). SAP PO Cloud Migration: Architecture, Business Value, and Impact on Connected Systems. International Journal of Humanities and Information Technology, 4(01-03), 53-66.
19. Singh, A. (2023). Self-evolving IoT systems through edge-based autonomous learning. International Journal of Engineering & Extended Technologies Research (IJEETR), 5(6), 7547–7555. https://doi.org/10.15662/IJEETR.2023.0506011
20. Navandar, P. (2022). SMART: Security Model Adversarial Risk-based Tool. International Journal of Research and Applied Innovations, 5(2), 6741-6752.
21. Samal, B. (2025). Mathematical Framework for ABM-MARL Integration in Financial Systems: A Discrete Multi-Agent Population-Strategy Game Approach. https://www.researchsquare.com/article/rs-7326746/v1
22. Kabade, S., Sharma, A., & Chaudhari, B. B. (2025, June). Tailoring AI and Cloud in Modern Enterprises to Enhance Enterprise Architecture Governance and Compliance. In 2025 5th International Conference on Intelligent Technologies (CONIT) (pp. 1-6). IEEE.
23. Vimal Raja, G. (2022). Leveraging Machine Learning for Real-Time Short-Term Snowfall Forecasting Using MultiSource Atmospheric and Terrain Data Integration. International Journal of Multidisciplinary Research in Science, Engineering and Technology, 5(8), 1336-1339.
24. Kim, S., & Lee, H. (2018). Machine learning-based anomaly detection in enterprise networks. Journal of Network and Computer Applications, 113, 1–14. https://doi.org/10.1016/j.jnca.2018.03.002
25. Nagarajan, G. (2024). Cloud-Integrated AI Models for Enhanced Financial Compliance and Audit Automation in SAP with Secure Firewall Protection. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 7(1), 9692-9699.
26. Adari, V. K. (2020). Intelligent Care at Scale AI-Powered Operations Transforming Hospital Efficiency. International Journal of Engineering & Extended Technologies Research (IJEETR), 2(3), 1240-1249.
27. Sudhan, S. K. H. H., & Kumar, S. S. (2016). Gallant Use of Cloud by a Novel Framework of Encrypted Biometric Authentication and Multi Level Data Protection. Indian Journal of Science and Technology, 9, 44.
28. Poornima, G., & Anand, L. (2024, April). Effective strategies and techniques used for pulmonary carcinoma survival analysis. In 2024 1st International Conference on Trends in Engineering Systems and Technologies (ICTEST) (pp. 1-6). IEEE.
29. Sugumar, R. (2024). AI-Driven Cloud Framework for Real-Time Financial Threat Detection in Digital Banking and SAP Environments. International Journal of Technology, Management and Humanities, 10(04), 165-175.
30. Kumar, S. S. (2024). Cybersecure Cloud AI Banking Platform for Financial Forecasting and Analytics in Healthcare Systems. International Journal of Humanities and Information Technology, 6(04), 54-59.
31. Wang, Y., & Li, M. (2021). Cloud-native architectures for enterprise AI integration. Journal of Cloud Computing, 10(2), 89–104. https://doi.org/10.1186/s13677-021-00289-5
32. Kubam, C. S. (2025). Agentic AI for Autonomous, Explainable, and Real-Time Credit Risk Decision-Making. arXiv preprint arXiv:2601.00818.