Cognitive Cloud-Native Banking Ecosystem: AI-Powered Deep Neural Networks and Quantum SAP Integration for Reliable Financial Operations
DOI:
https://doi.org/10.15662/IJARCST.2025.0806012Keywords:
AI-Powered Banking, Cloud-Native Architecture, Deep Neural Networks, BERT Models, Quantum-Optimized SAP, Financial Intelligence, Quality Assurance, Intelligent Automation, Natural Language Processing, Predictive Analytics, Risk Management, Cloud Computing, FinTech Innovation, Software Reliability, Cognitive Banking EcosystemAbstract
The evolution of digital banking demands advanced intelligence, scalability, and trust in automated financial systems. This paper presents an AI-powered cloud-native banking ecosystem that integrates Deep Neural Networks (DNNs), Bidirectional Encoder Representations from Transformers (BERT), and Quantum-Optimized SAP frameworks to achieve intelligent, reliable, and quality-assured financial operations. The proposed architecture leverages cloud-native microservices and containerized deployment to ensure scalability, interoperability, and resilience within heterogeneous banking environments. BERT-based Natural Language Processing (NLP) modules enable contextual understanding of financial documents, customer interactions, and compliance reports, while deep neural models enhance fraud detection, credit scoring, and risk forecasting accuracy. The Quantum-optimized SAP layer accelerates data analytics and transactional performance, offering high-throughput financial computation and real-time decision support. Furthermore, the incorporation of AI-driven quality assurance (AI-QA) ensures the reliability, transparency, and ethical compliance of automated processes across the financial workflow. Experimental evaluations demonstrate improved data accuracy, operational efficiency, and model explainability within complex financial ecosystems. This research establishes a next-generation framework for intelligent, cloud-native banking transformation, uniting AI, quantum computing, and software quality assurance to redefine digital financial operations with enhanced performance, trust, and interpretability.
References
1. Bengio, Y., Courville, A., & Vincent, P. (2013). Representation learning: A review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(8), 1798–1828.
2. 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.
3. Prabaharan, G., Sankar, S. U., Anusuya, V., Deepthi, K. J., Lotus, R., & Sugumar, R. (2025). Optimized disease prediction in healthcare systems using HDBN and CAEN framework. MethodsX, 103338.
4. Thambireddy, S., Bussu, V. R. R., & Pasumarthi, A. (2022). Engineering Fail-Safe SAP Hana Operations in Enterprise Landscapes: How SUSE Extends Its Advanced High-Availability Framework to Deliver Seamless System Resilience, Automated Failover, and Continuous Business Continuity. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(3), 6808-6816.
5. Shor, P. W. (1994). Algorithms for quantum computation: Discrete logarithms and factoring. In Proceedings 35th Annual Symposium on Foundations of Computer Science (pp. 124–134). IEEE.
6. Schuld, M., Sinayskiy, I., & Petruccione, F. (2015). An introduction to quantum machine learning. Contemporary Physics, 56(2), 172–185.
7. Arute, F., Arya, K., Babbush, R., et al. (2019). Quantum supremacy using a programmable superconducting processor. Nature, 574(7779), 505–510.
8. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 770–778).
9. Silver, D., Schrittwieser, J., Simonyan, K., et al. (2017). Mastering the game of Go without human knowledge. Nature, 550(7676), 354–359.
10. Batchu, K. C. (2023). Cross-Platform ETL Federation: A Unified Interface for Multi-Cloud Data Integration. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(6), 9632-9637.
11. Gosangi, S. R. (2024). Secure and Scalable Single Sign-On Architecture for Large-Scale Enterprise Environments. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(3), 10466-10471.
12. 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.
13. Montanaro, A. (2016). Quantum algorithms: An overview. npj Quantum Information, 2(1), 15023.
14. Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: A simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research, 15(1), 1929–1958.
15. Deng, L., & Yu, D. (2014). Deep learning: Methods and applications. Foundations and Trends in Signal Processing, 7(3–4), 197–387.
16. Yan, X., Zhou, J., Zhang, J., & Qi, X. (2020). Quantum machine learning in finance: A review. Quantum Machine Intelligence, 2(2), 1–14.
17. Kondra, S., Raghavan, V., & kumar Adari, V. (2025). Beyond Text: Exploring Multimodal BERT Models. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 8(1), 11764-11769.
18. Christadoss, J., Das, D., & Muthusamy, P. (2025). AI-Agent Driven Test Environment Setup and Teardown for Scalable Cloud Applications. Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online), 4(3), 1-13.
19. Komarina, G. B. (2024). Transforming Enterprise Decision-Making Through SAP S/4HANA Embedded Analytics Capabilities. Journal ID, 9471, 1297.
20. Zerine, I., Biswas, Y. A., Doha, Z., Meghla, H. M., & Polas, M. R. H. (2025). Understanding Behavioral Intentions to Use Cryptocurrency for the Future of Digital Finance: Evidence from Bangladesh. Journal of Comprehensive Business Administration Research.
21. 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
22. Wang, S., & Xu, Z. (2021). Deep learning for financial applications: Recent advances and future directions. Journal of Financial Data Science, 3(2), 12–34.
23. Kumbum, P. K., Adari, V. K., Chunduru, V. K., Gonepally, S., & Amuda, K. K. (2020). Artificial intelligence using TOPSIS method. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 3(6), 4305-4311.
24. Manivannan, R., Sugumar, R., & Vijayabharathi, R. (2025, May). A Convolutional Deep Learning Method for Digital Image Processing in the Identification of Vitamin Deficiencies. In 2025 International Conference on Computational Robotics, Testing and Engineering Evaluation (ICCRTEE) (pp. 1-6). IEEE.
25. Kshetri, N. (2018). 1 Blockchain’s roles in meeting key supply chain management objectives. International Journal of Information Management, 39, 80–89.
