Human-Centric AI Cloud Banking: Quantum-Enhanced SAP Integration for Intelligent Financial Services
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Abstract
The convergence of artificial intelligence, cloud computing, and quantum technologies is reshaping the banking sector, enabling faster, smarter, and more secure financial services. This study presents a human-centric AI cloud banking framework that integrates SAP platforms with quantum-enhanced computing capabilities to optimize financial operations, decision-making, and risk management. By placing human experience and operational efficiency at the core, the framework leverages AI-driven analytics for predictive insights, automated workflows, and personalized banking solutions. Quantum computing accelerates complex computations, enhancing real-time data processing and predictive accuracy. Experimental evaluations demonstrate significant improvements in transaction efficiency, risk assessment, and customer-centric service delivery, highlighting the potential of human-centric AI-quantum cloud ecosystems for next-generation intelligent banking.
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1. “Improved financial forecasting via quantum machine learning”. (2024). Quantum Machine Intelligence, 6, Article 27. https://doi.org/10.1007/s42484 024 00157 0
2. Chunduru, V. K., Gonepally, S., Amuda, K. K., Kumbum, P. K., & Adari, V. K. (2022). Evaluation of human information processing: An overview for human-computer interaction using the EDAS method. SOJ Materials Science & Engineering, 9(1), 1–9.
3. “Quantum Computing and AI”. (2024, September 18). KI Künstliche Intelligenz, 38, 251–255. https://doi.org/10.1007/s13218 024 00872 7
4. Sugumar, R. (2023, September). A Novel Approach to Diabetes Risk Assessment Using Advanced Deep Neural Networks and LSTM Networks. In 2023 International Conference on Network, Multimedia and Information Technology (NMITCON) (pp. 1-7). IEEE.
5. Venkata Ramana Reddy Bussu,, Sankar, Thambireddy, & Balamuralikrishnan Anbalagan. (2023). EVALUATING THE FINANCIAL VALUE OF RISE WITH SAP: TCO OPTIMIZATION AND ROI REALIZATION IN CLOUD ERP MIGRATION. International Journal of Engineering Technology Research & Management (IJETRM), 07(12), 446–457. https://doi.org/10.5281/zenodo.15725423
6. Pashikanti, S. (2023). Leveraging generative AI for business transformation: A multi cloud perspective. International Journal of Core Engineering & Management, 7(07).
7. Adigun, P. O., Oyekanmi, T. T., & Adeniyi, A. A. (2023). Simulation Prediction of Background Radiation Using Machine Learning. New Mexico Highlands University.
8. Sangannagari, S. R. (2023). Smart Roofing Decisions: An AI-Based Recommender System Integrated into RoofNav. International Journal of Humanities and Information Technology, 5(02), 8-16.
9. Kiran Nittur, Srinivas Chippagiri, Mikhail Zhidko, “Evolving Web Application Development Frameworks: A Survey of Ruby on Rails, Python, and Cloud-Based Architectures”, International Journal of New Media Studies (IJNMS), 7 (1), 28-34, 2020.
10. Joyce, S., Pasumarthi, A., & Anbalagan, B. SECURITY OF SAP SYSTEMS IN AZURE: ENHANCING SECURITY POSTURE OF SAP WORKLOADS ON AZURE–A COMPREHENSIVE REVIEW OF AZURE-NATIVE TOOLS AND PRACTICES.
11. Adimulam, T. (2022). Scalable architectures for generative AI in advanced cloud computing environments: Enhancing performance and efficiency. IJCRT, 10(9).
12. 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.
13. Nallamothu, T. K. (2024). Real-Time Location Insights: Leveraging Bright Diagnostics for Superior User Engagement. International Journal of Technology, Management and Humanities, 10(01), 13-23.
14. Balaji, K. V., & Sugumar, R. (2022, December). A Comprehensive Review of Diabetes Mellitus Exposure and Prediction using Deep Learning Techniques. In 2022 International Conference on Data Science, Agents & Artificial Intelligence (ICDSAAI) (Vol. 1, pp. 1-6). IEEE.
15. Jabed, M. M. I., Khawer, A. S., Ferdous, S., Niton, D. H., Gupta, A. B., & Hossain, M. S. (2023). Integrating Business Intelligence with AI-Driven Machine Learning for Next-Generation Intrusion Detection Systems. International Journal of Research and Applied Innovations, 6(6), 9834-9849.
16. Kumbum, P. K., Adari, V. K., Chunduru, V. K., Gonepally, S., & Amuda, K. K. (2020). Artificial intelligence using TOPSIS method. Journal of Computer Science Applications and Information Technology, 5(1), 1–7. https://doi.org/10.15226/2474-9257/5/1/00147
17. Gosangi, S. R. (2023). Transforming Government Financial Infrastructure: A Scalable ERP Approach for the Digital Age. International Journal of Humanities and Information Technology, 5(01), 9-15.
18. Cherukuri, B. R. (2024). Serverless computing: How to build and deploy applications without managing infrastructure. World Journal of Advanced Engineering Technology and Sciences, 11(2).
19. “Quantum AI (QAI): Harnessing quantum computing for AI (2024 update)”. (2024). PostQuantum.com. Retrieved from https://postquantum.com/quantum ai qai/
20. 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.
21. Joseph, Jimmy. (2024). AI-Driven Synthetic Biology and Drug Manufacturing Optimization. International Journal of Innovative Research in Computer and Communication Engineering. 12. 1138. 10.15680/IJIRCCE.2024.1202069.
22. “Intelligent network optimisation in cloud environments with generative AI and LLMs [v1]”. (2024). Preprints.org.