AI-Driven SAP HANA Cloud Platform Enabling Scalable Data Migration and Healthcare Big Data Analytics with Real-Time Fraud Detection
DOI:
https://doi.org/10.15680/IJCTECE.2025.0805802Keywords:
SAP HANA Cloud, data center migration, healthcare big data, machine learning, fraud detection, AI-driven analytics, data quality engineering, real-time processing, in-memory computing, cloud transformation, predictive analytics, healthcare IT modernizationAbstract
Healthcare enterprises increasingly rely on high-performance cloud platforms to handle massive data workloads, ensure regulatory compliance, prevent fraud, and support clinical decision-making. Traditional on-premises systems are strained by rapid data growth, complex interoperability needs, and computational demands of advanced machine learning (ML). This research proposes an AI-driven SAP HANA Cloud Platform designed to streamline scalable data center migration, improve healthcare big data quality, and enable real-time ML-powered fraud detection. By leveraging in-memory processing, multi-model data architecture, AI integration services, and cloud elasticity, SAP HANA Cloud offers a unified solution to modern healthcare challenges. The proposed framework focuses on four pillars: (1) seamless and secure data center migration, (2) healthcare big data governance and quality engineering, (3) ML-powered real-time analytics, and (4) anomaly- and pattern-based fraud detection. Simulated experiments and architectural evaluations demonstrate significant improvements in data latency, analytics performance, data accuracy, and fraud detection precision. The study confirms that an AI-driven SAP HANA Cloud infrastructure can serve as a strategic enabler for healthcare organizations seeking digital transformation, operational efficiency, and robust security. The research concludes by presenting best practices, measurable improvements, and an enterprise blueprint for large-scale cloud modernization.
References
1. HV, M. S., & Kumar, S. S. (2024). Fusion Based Depression Detection through Artificial Intelligence using Electroencephalogram (EEG). Fusion: Practice & Applications, 14(2).
2. Pichaimani, T., Ratnala, A. K., & Parida, P. R. (2024). Analyzing time complexity in machine learning algorithms for big data: a study on the performance of decision trees, neural networks, and SVMs. Journal of Science & Technology, 5(1), 164-205.
3. Shashank, P. S. R. B., Anand, L., & Pitchai, R. (2024, December). MobileViT: A Hybrid Deep Learning Model for Efficient Brain Tumor Detection and Segmentation. In 2024 International Conference on Progressive Innovations in Intelligent Systems and Data Science (ICPIDS) (pp. 157-161). IEEE.
4. Mani, R. (2024). Smart Resource Management in SAP HANA: A Comprehensive Guide to Workload Classes, Admission Control, and System Optimization through Memory, CPU, and Request Handling Limits. International Journal of Research and Applied Innovations, 7(5), 11388-11398.
5. Adari, V. K., Chunduru, V. K., Gonepally, S., Amuda, K. K., & Kumbum, P. K. (2023). Ethical analysis and decision-making framework for marketing communications: A weighted product model approach. Data Analytics and Artificial Intelligence, 3 (5), 44–53.
6. Kusumba, S. (2022). Cloud-Optimized Intelligent ETL Framework for Scalable Data Integration in Healthcare–Finance Interoperability Ecosystems. International Journal of Research and Applied Innovations, 5(3), 7056-7065.
7. Mahajan, A. S. (2025). INTEGRATING DATA ANALYTICS AND ECONOMETRICS FOR PREDICTIVE ECONOMIC MODELLING. International Journal of Applied Mathematics, 38(2s), 1450-1462.
8. Kumar, R. K. (2024). Real-time GenAI neural LDDR optimization on secure Apache–SAP HANA cloud for clinical and risk intelligence. IJEETR, 8737–8743. https://doi.org/10.15662/IJEETR.2024.0605006
9. Balaji, K. V., & Sugumar, R. (2023, December). Harnessing the Power of Machine Learning for Diabetes Risk Assessment: A Promising Approach. In 2023 International Conference on Data Science, Agents & Artificial Intelligence (ICDSAAI) (pp. 1-6). IEEE.
10. Nagarajan, G. (2023). AI-Integrated Cloud Security and Privacy Framework for Protecting Healthcare Network Information and Cross-Team Collaborative Processes. International Journal of Engineering & Extended Technologies Research (IJEETR), 5(2), 6292-6297.
11. Kagalkar, A., Sharma, A., Chaudhri, B., & Kabade, S. (2024). AI-Powered Pension Ecosystems: Transforming Claims, Payments, and Member Services. International Journal of AI, BigData, Computational and Management Studies, 5(4), 145-150.
12. Rajurkar, P. AI-Driven Fenceline Monitoring for Real-Time Detection of Hazardous Air Pollutants in Industrial Corridors. (Tjosvold, 1998)
13. Sivaraju, P. S. (2024). Cross-functional program leadership in multi-year digital transformation initiatives: Bridging architecture, security, and operations. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 7(6), 11374-11380.
14. Sen, S., Kurni, M., Krishnamaneni, R., & Murthy, A. (2024, December). Improved Bi-directional Long Short-Term Memory for Heart Disease Diagnosis using Statistical and Entropy Feature Set. In 2024 9th International Conference on Communication and Electronics Systems (ICCES) (pp. 1331-1337). IEEE.
15. Kalyanasundaram, P. D., & Paul, D. (2023). Secure AI Architectures in Support of National Safety Initiatives: Methods and Implementation. Newark Journal of Human-Centric AI and Robotics Interaction, 3, 322-355.
16. Christadoss, J., & Panda, M. R. (2025). Exploring the Role of Generative AI in Making Distance Education More Interactive and Personalised through Simulated Learning. Futurity Proceedings, (4), 114-127.
17. Meka, S. (2024). Securing Instant Payments: Implementing Fraud Prevention Frameworks with AVS and OTP Validation. Journal Code, 1763, 4821.
18. Sukla, R. R. (2025). Continuous Quality Automation: Transforming Software Development Practices. Journal Of Multidisciplinary, 5(7), 361-367.
19. Joyce, S., Pasumarthi, A., & Anbalagan, B. (2025). SECURITY OF SAP SYSTEMS IN AZURE: ENHANCING SECURITY POSTURE OF SAP WORKLOADS ON AZURE–A COMPREHENSIVE REVIEW OF AZURENATIVE TOOLS AND PRACTICES.||.
20. Gujjala, Praveen Kumar Reddy. (2024). Optimizing ETL Pipelines with Delta Lake and Medallion Architecture: A Scalable Approach for Large-Scale Data. International Journal For Multidisciplinary Research. 6. 10.36948/ijfmr.2024.v06i06.55445.
21. Singh, N. N. (2025). Identity-Centric Security in the SaaS-Driven Enterprise: Balancing User Experience and Risk with Okta+ Google Workspace. Journal of Computer Science and Technology Studies, 7(9), 87-96.
22. Dhanorkar, T., Vijayaboopathy, V., & Das, D. (2020). Semantic Precedent Retriever for Rapid Litigation Strategy Drafting. Journal of Artificial Intelligence & Machine Learning Studies, 4, 71-109.
23. Chandra Sekhar Oleti, " Real-Time Feature Engineering and Model Serving Architecture using Databricks Delta Live Tables" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 9, Issue 6, pp.746-758, November-December-2023. Available at doi : https://doi.org/10.32628/CSEIT23906203
24. Sandeep Kamadi. (2022). AI-Powered Rate Engines: Modernizing Financial Forecasting Using Microservices and Predictive Analytics. International Journal of Computer Engineering and Technology (IJCET), 13(2), 220-233.
25. Prakash, R. (2023). Neural fraud detection models.
26. Godleti, S. B. (2025). Taming Spark Data Skew with Practical Solutions. Journal of Computer Science and Technology Studies, 7(6), 752-758.
27. Achari, A. P. S. K., & Sugumar, R. (2024, November). Performance analysis and determination of accuracy using machine learning techniques for naive bayes and random forest. In AIP Conference Proceedings (Vol. 3193, No. 1, p. 020199). AIP Publishing LLC.
28. Adari, V. K. (2021). Building trust in AI-first banking: Ethical models, explainability, and responsible governance. International Journal of Research and Applied Innovations (IJRAI), 4(2), 4913–4920. https://doi.org/10.15662/IJRAI.2021.0402004
29. Nadiminty, Y. (2025). Accelerating Cloud Modernization with Agentic AI. Journal of Computer Science and Technology Studies, 7(9), 26-35.
30. Padmanabham, S. (2025). Security and Compliance in Integration Architectures: A Framework for Modern Enterprises. International Journal of Computing and Engineering, 7(16), 45-55.
31. Sridhar Reddy Kakulavaram, Praveen Kumar Kanumarlapudi, Sudhakara Reddy Peram. (2024). Performance Metrics and Defect Rate Prediction Using Gaussian Process Regression and Multilayer Perceptron. International Journal of Information Technology and Management Information Systems (IJITMIS), 15(1), 37-53.
32. 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.
33. Chukkala, R. (2025). Unified Smart Home Control: AI-Driven Hybrid Mobile Applications for Network and Entertainment Management. Journal of Computer Science and Technology Studies, 7(2), 604-611.
34. Vasugi, T. (2022). AI-Enabled Cloud Architecture for Banking ERP Systems with Intelligent Data Storage and Automation using SAP. International Journal of Engineering & Extended Technologies Research (IJEETR), 4(1), 4319-4325.
35. Navandar, P. (2022). SMART: Security Model Adversarial Risk-based Tool. International Journal of Research and Applied Innovations, 5(2), 6741-6752.
36. Girdhar, P., Virmani, D., & Saravana Kumar, S. (2019). A hybrid fuzzy framework for face detection and recognition using behavioral traits. Journal of Statistics and Management Systems, 22(2), 271-287.
