Next-Generation Quantum Cloud AI for Real-Time Financial Analytics and Quality Assurance in SAP Systems
DOI:
https://doi.org/10.15662/IJARCST.2025.0806011Keywords:
Quantum Computing, Cloud AI, SAP Systems, Real-Time Analytics, Financial Data Management, Quality Assurance, Intelligent Automation, Predictive ModelingAbstract
The increasing complexity of financial operations in digital enterprises demands advanced, intelligent, and high-performance solutions that ensure both analytical precision and operational reliability. This paper proposes a Next-Generation Quantum Cloud AI Framework designed to deliver real-time financial analytics and quality assurance within SAP-based enterprise systems. By integrating quantum-inspired algorithms with cloud-native AI services, the framework accelerates data computation, enhances predictive modeling, and strengthens decision accuracy across dynamic financial processes. The use of real-time data streaming and automated ETL pipelines enables continuous monitoring of transactional integrity and compliance adherence, ensuring robust and transparent financial governance. In addition, the incorporation of AI-driven anomaly detection improves quality assurance cycles by identifying inconsistencies and optimizing test case execution. Deployed in a hybrid cloud environment, the system leverages elasticity and scalability while maintaining strong data security and privacy controls. Experimental validation demonstrates significant gains in processing speed, analytical accuracy, and testing efficiency, marking a major advancement toward intelligent, quantum-ready financial ecosystems in SAP environments.
References
1. Zheng, X., Zhu, M., Li, Q., Chen, C., & Tan, Y. (2018). FinBrain: When Finance Meets AI 2.0. arXiv. arXiv
2. Slominski, A., Muthusamy, V., & Ishakian, V. (2019). Towards enterprise ready AI deployments minimizing the risk of consuming AI models in business applications. arXiv. arXiv
3. Sugumar, Rajendran (2024). Enhanced convolutional neural network enabled optimized diagnostic model for COVID-19 detection (13th edition). Bulletin of Electrical Engineering and Informatics 13 (3):1935-1942.
4. Anand, L., Krishnan, M. M., Senthil Kumar, K. U., & Jeeva, S. (2020, October). AI multi agent shopping cart system based web development. In AIP Conference Proceedings (Vol. 2282, No. 1, p. 020041). AIP Publishing LLC.
5. Komarina, G. B. (2024). Transforming Enterprise Decision-Making Through SAP S/4HANA Embedded Analytics Capabilities. Journal ID, 9471, 1297.
6. 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.
7. Jannatul, F., Md Saiful, I., Md, S., & Gul Maqsood, S. (2025). AI-Driven Investment Strategies Ethical Implications and Financial Performance in Volatile Markets. American Journal of Business Practice, 2(8), 21-51.
8. Jelani, U., & Perveen, K. (2024). Cloud Native Architectures: Building and Managing Applications at Scale. Int. J. Machine Learning Research in Cybersecurity & AI, 15(1). ijmlrcai.com
9. Batchu, K. C. (2025). Next-Generation Cloud ETL Pipelines: A Comparative Study of Serverless and Containerized Architectures. Journal Of Multidisciplinary, 5(7), 411-417.
10. Oyeniran, O. C., Adewusi, A. O., Adeleke, A. G., Akwawa, L. A., & Azubuko, C. F. (2024). Microservices Architecture in Cloud Native Applications: Design Patterns and Scalability. Int. J. Advanced Research & Interdisciplinary Scientific Endeavours, 1(2). ijarise.org
11. Archana, R., & Anand, L. (2023, May). Effective Methods to Detect Liver Cancer Using CNN and Deep Learning Algorithms. In 2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI) (pp. 1-7). IEEE.
12. Sivaraju, P. S. (2024). PRIVATE CLOUD DATABASE CONSOLIDATION IN FINANCIAL SERVICES: A CASE STUDY OF DEUTSCHE BANK APAC MIGRATION. ITEGAM-Journal of Engineering and Technology for Industrial Applications (ITEGAM-JETIA).
13. Devarashetty, P. K. Unlocking Long-Term Value: A Multi-Stakeholder Perspective on Post-Implementation Success in SAP ERP Systems. IJAIDR-Journal of Advances in Developmental Research, 15(1).
14. Sugumar R (2014) A technique to stock market prediction using fuzzy clustering and artificial neural networks. Comput Inform 33:992–1024
15. Karanjkar, R., & Karanjkar, D. (2024). Optimizing Quality Assurance Resource Allocation in Multi Team Software Development Environments. International Journal of Technology, Management and Humanities, 10(04), 49-59.
16. Arjunan, T., Arjunan, G., & Kumar, N. J. (2025, May). Optimizing Quantum Support Vector Machine (QSVM) Circuits Using Hybrid Quantum Natural Gradient Descent (QNGD) and Whale Optimization Algorithm (WOA). In 2025 6th International Conference for Emerging Technology (INCET) (pp. 1-7). IEEE
17. R. Sugumar, A. Rengarajan and C. Jayakumar, Design a Weight Based Sorting Distortion Algorithm for Privacy Preserving Data Mining, Middle-East Journal of Scientific Research 23 (3): 405-412, 2015.
18. Bomma Reddy, A. R. (2024). AI Enhanced Microservice Security in Cloud Based Financial Platforms: A Case Study of AWS Implementation. Int. J. Sci. Res. in CS, Eng. & IT, 10(6), 1268–1279. IJSRCSEIT
19. Christadoss, J., Kalyanasundaram, P. D., & Vunnam, N. (2024). Hybrid GraphQL-FHIR Gateway for Real-Time Retail-Health Data Interchange. Essex Journal of AI Ethics and Responsible Innovation, 4, 204-238.
20. Karthick, T., Gouthaman, P., Anand, L., & Meenakshi, K. (2017, August). Policy based architecture for vehicular cloud. In 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS) (pp. 118-124). IEEE.
21. Abdul Azeem, M., Tanvir Rahman, A., & Ismoth, Z. (2022). BUSINESS RULES AUTOMATION THROUGH ARTIFICIAL INTELLIGENCE: IMPLICATIONS ANALYSIS AND DESIGN. International Journal of Economy and Innovation, 29, 381-404.
22. Adari, V. K. (2024). The Path to Seamless Healthcare Data Exchange: Analysis of Two Leading Interoperability Initiatives. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(6), 11472-11480.
23. Bhole, A. (2023). Cloud Native Architecture and Microservices. J. Artificial Intelligence, Machine Learning & Data Science, 1(2), 2032 2037. urfjournals.org
24. Pasumarthi, A., & Joyce, S. (2025). Leveraging SAP’s Business Technology Platform (BTP) for Enterprise Digital Transformation: Innovations, Impacts, and Strategic Outcomes. International Journal of Computer Technology and Electronics Communication, 8(3), 10720-10732.
25. Kumar, R., Al-Turjman, F., Anand, L., Kumar, A., Magesh, S., Vengatesan, K., ... & Rajesh, M. (2021). Genomic sequence analysis of lung infections using artificial intelligence technique. Interdisciplinary Sciences: Computational Life Sciences, 13(2), 192-200.
26. Vadde, B. C., & Munagandla, V. B. (2024). Cloud Native DevOps: Leveraging Microservices and Kubernetes for Scalable Infrastructure. Int. J. Machine Learning Research in Cybersecurity & AI, 15(1), 545 554. ijmlrcai.com
27. Challa, S. R. (2023). Revolutionizing Wealth Management: The Role of AI, Machine Learning and Big Data in Personalized Financial Services. European Modern Studies Journal. (pre print) (Note: reference for context)
