Intelligent Cloud–AI Platform for Risk-Aware Healthcare Operations Using SAP and Machine Learning Models
DOI:
https://doi.org/10.15662/IJRAI.2025.0806804Keywords:
Artificial Intelligence (AI), Cloud Computing, SAP Integration, Machine Learning (ML), Healthcare Risk Management, Building Management System (BMS), Predictive AnalyticsAbstract
The rapid digital transformation of the healthcare sector demands intelligent systems capable of managing operational risk, optimizing resources, and ensuring data-driven decision-making. This study proposes an Intelligent Cloud–AI Platform that integrates SAP enterprise systems with Machine Learning (ML) models to enhance healthcare Building Management Systems (BMS). The framework leverages cloud computing for scalable data processing and AI algorithms for predictive analytics, enabling early identification of clinical and administrative risks. By embedding ML models within the SAP environment, the platform supports automated workflow optimization, risk prediction, and real-time monitoring of patient care and operational performance. Experimental evaluation demonstrates significant improvements in risk detection accuracy, data transparency, and overall process efficiency. The proposed system provides a secure, adaptable foundation for risk-aware healthcare operations, fostering intelligent automation and sustainable decision support across healthcare organizations.
References
1. Adwan, E. J., & Alsaeed, B. A. (2022). Cloud computing adoption in the financial banking sector A systematic literature review (2011 2021). International Journal of Advanced Science Computing and Engineering, 4(1), 48–55. IJASCE+1
2. 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.
3. 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.
4. Reddy, B. V. S., & Sugumar, R. (2025, April). Improving dice-coefficient during COVID 19 lesion extraction in lung CT slice with watershed segmentation compared to active contour. In AIP Conference Proceedings (Vol. 3270, No. 1, p. 020094). AIP Publishing LLC.
5. Manda, P. (2023). LEVERAGING AI TO IMPROVE PERFORMANCE TUNING IN POST-MIGRATION ORACLE CLOUD ENVIRONMENTS. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(3), 8714-8725.
6. Fares, O. H., Butt, I., & Lee, S. H. M. (2022). Utilization of artificial intelligence in the banking sector: A systematic literature review. Journal of Financial Services Marketing. PMC
7. Nurtaz Begum, A., Samira Alam, C., & KM, Z. (2025). Enhancing Data Privacy in National Business Infrastructure: Measures that Concern the Analytics and Finance Industry. American Journal of Technology Advancement, 2(10), 46-54.
8. Poornima, G., & Anand, L. (2025). Medical image fusion model using CT and MRI images based on dual scale weighted fusion based residual attention network with encoder-decoder architecture. Biomedical Signal Processing and Control, 108, 107932.
9. 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.
10. Phani Santhosh Sivaraju, 2025. "Phased Enterprise Data Migration Strategies: Achieving Regulatory Compliance in Wholesale Banking Cloud Transformations," Journal of Artificial Intelligence General science (JAIGS) ISSN:3006- 4023, Open Knowledge, vol. 8(1), pages 291-306.
11. Adari, V. K. (2024). APIs and open banking: Driving interoperability in the financial sector. International Journal of Research in Computer Applications and Information Technology (IJRCAIT), 7(2), 2015–2024.
12. Konda, S. K. (2022). ENGINEERING RESILIENT INFRASTRUCTURE FOR BUILDING MANAGEMENT SYSTEMS: NETWORK RE-ARCHITECTURE AND DATABASE UPGRADE AT NESTLÉ PHX. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(1), 6186-6201.
13. 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.
14. Christadoss, J., Panda, M. R., Samal, B. V., & Wali, G. (2025). Development of a Multi-Objective Optimisation Framework for Risk-Aware Fractional Investment Using Reinforcement Learning in Retail Finance. Futurity Proceedings, 3.
15. Aladiyan, A. (Year). Revolutionizing financial services: AI and cloud connectivity for improved customer service. International Journal of Intelligent Systems and Applications in Engineering. IJISAE
16. Patel, N., & Jagli, D. (2023). The impact of cloud computing in the field of finance: A comprehensive analysis. IJRASET. IJRASET
17. Ragula, A. (2022). Cloud computing in finance: enabling secure and scalable banking services. Journal of Advanced Research Engineering and Technology (JARET). Lib Index
18. Balaji, P. C., & Sugumar, R. (2025, June). Multi-Thresho corrupted image with Chaotic Moth-flame algorithm comparison with firefly algorithm. In AIP Conference Proceedings (Vol. 3267, No. 1, p. 020179). AIP Publishing LLC.
19. 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.
20. Gosangi, S. R. (2023). AI AND THE FUTURE OF PUBLIC SECTOR ERP: INTELLIGENT AUTOMATION BEYOND DATA ANALYTICS. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(4), 8991-8995.
21. 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
22. PIFS. (2019). Cloud computing in the financial sector: A global perspective. PIFS International Review. pifsinternational.org
