Real-Time AI-Cloud Framework for Financial Analytics in SAP and Oracle-Integrated Systems using Deep Learning
DOI:
https://doi.org/10.15662/IJRAI.2025.0806011Keywords:
AI-Cloud Framework, Real-Time Financial Analytics, SAP Integration, Oracle Systems, Deep Learning, Predictive Intelligence, Business Management SystemsAbstract
This paper presents a Real-Time AI-Cloud Framework designed to enhance financial analytics in SAP and Oracle-integrated business environments through the implementation of deep learning techniques. The proposed architecture leverages the power of artificial intelligence and cloud computing to process large-scale financial data with high accuracy and minimal latency. By integrating deep learning models within SAP and Oracle ecosystems, the framework enables automated data extraction, pattern recognition, and predictive financial insights in real time. The system utilizes cloud-based orchestration to ensure scalability, interoperability, and resilience across distributed enterprise infrastructures. Furthermore, it addresses challenges related to data consistency, model adaptability, and system responsiveness, enabling intelligent decision-making and financial risk mitigation. Experimental evaluations demonstrate that the proposed framework significantly improves forecasting precision, operational efficiency, and real-time analytical capabilities. This research contributes to the advancement of intelligent, adaptive, and data-driven financial management systems in modern enterprise ecosystems.
References
1. Cao, L., & Tay, F. E. H. (2001). Financial forecasting using support vector machines. Neural Computing and Applications, 10(2), 184–192. https://doi.org/10.1007/s005210170010 DeepDyve+1
2. Konda, S. K. (2023). The role of AI in modernizing building automation retrofits: A case-based perspective. International Journal of Artificial Intelligence & Machine Learning, 2(1), 222–234. https://doi.org/10.34218/IJAIML_02_01_020
3. 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.
4. Balaji, P. C., & Sugumar, R. (2025, April). Accurate thresholding of grayscale images using Mayfly algorithm comparison with Cuckoo search algorithm. In AIP Conference Proceedings (Vol. 3270, No. 1, p. 020114). AIP Publishing LLC.
5. Sivaraju, P. S., & Mani, R. (2024). Private Cloud Database Consolidation in Financial Services: A Comprehensive Case Study on APAC Financial Industry Migration and Modernization Initiatives. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(3), 10472-10490.
6. 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.
7. Mani, R., & Pasumarthi, A. (2025). End-to-End SAP S/4HANA Rise Migration to SAP Cloud: Architecting a Secure and Scalable Landscape with Cloud Connector, Landscape Migration Server, SLT Server, Cloud Integration, and Governance Framework. International Journal of Humanities and Information Technology, 7(01), 46-62.
8. Gupta, D., Pratama, M., Ma, Z., Li, J., & Prasad, M. (2019). Financial time series forecasting using twin support vector regression. PLoS ONE, 14(3), e0211402. PMC
9. Kaushik, R., Jain, S., & Jain, S., & Dash, T. (2019). Performance evaluation of deep neural networks for forecasting time series with multiple structural breaks and high volatility. arXiv preprint arXiv:1911.06704. arXiv
10. Ryll, L., & Seidens, S. (2019). Evaluating the performance of machine learning algorithms in financial market forecasting: A comprehensive survey. arXiv preprint arXiv:1906.07786. arXiv
11. Reddy, B. V. S., & Sugumar, R. (2025, June). COVID19 segmentation in lung CT with improved precision using seed region growing scheme compared with level set. In AIP Conference Proceedings (Vol. 3267, No. 1, p. 020154). AIP Publishing LLC.
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. 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.
14. 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.
15. Zhang, P., Zhou, Z., & Wu, R. (Year unknown). Analyzing and predicting financial time series data using recurrent neural networks. Journal of Industrial Engineering and Applied Science. suaspress.org
16. Wong, K. F., & Selvi, S. (2013). Financial performance forecasting using support vector machines: A comparative study. Atlantis Press (per document). Atlantis Press
17. Rodrigues, P. C. (2022). Neural networks for financial time series forecasting. Entropy, 24(5), 657. MDPI
18. Chen, W., Hussain, W., Cauteruccio, F., & Zhang, X. (2023). Deep learning for financial time series prediction: A state of the art review of standalone and hybrid models. Computer Modeling in Engineering & Sciences, 124, 187 224. OUCI
19. AKTER, S., ISLAM, M., FERDOUS, J., HASSAN, M. M., & JABED, M. M. I. (2023). Synergizing Theoretical Foundations and Intelligent Systems: A Unified Approach Through Machine Learning and Artificial Intelligence.
20. 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.
21. 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.
22. Hsu, M., et al. (2016). An extensive survey of ANN and SVM in financial forecasting. (As referenced in Johnson, 2021). IRep
23. Gosangi, S. R. (2022). SECURITY BY DESIGN: BUILDING A COMPLIANCE-READY ORACLE EBS IDENTITY ECOSYSTEM WITH FEDERATED ACCESS AND ROLE-BASED CONTROLS. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(3), 6802-6807.
24. 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.
25. Balaji, P. C., & Sugumar, R. (2025, June). Multi-level thresholding of RGB images using Mayfly algorithm comparison with Bat algorithm. In AIP Conference Proceedings (Vol. 3267, No. 1, p. 020180). AIP Publishing LLC.
26. 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.
27. Ahmad, S., & Ahmad, H. M. (2025). Green AI for Sustainable Employee Attrition Prediction: Balancing Energy Efficiency and Predictive Accuracy. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 8(3), 12155-12160.
28. Lee, S. Y., et al. (2014). Improved BP algorithm and wavelet neural networks for corn futures price forecasting. (As referenced in 2024 summary) journals.zeuspress.org
