AI Enabled SAP Decision Intelligence using Machine Learning for Secure Cloud Platforms and Financial Risk Detection with Fault Tolerant Integration Architectures
DOI:
https://doi.org/10.15662/IJRAI.2026.0901005Keywords:
AI-enabled SAP Decision Intelligence, machine learning, secure cloud platforms, financial risk detection, fault-tolerant architectures, cloud integration, enterprise analyticsAbstract
The rapid adoption of cloud-based enterprise systems has increased the demand for intelligent, secure, and resilient decision-making platforms. SAP Decision Intelligence, when enhanced with artificial intelligence (AI) and machine learning (ML), provides organizations with the capability to transform large-scale enterprise data into actionable insights. This paper explores an AI-enabled SAP Decision Intelligence framework deployed on secure cloud platforms to enhance financial risk detection and operational decision-making. Machine learning models are leveraged to identify anomalies, predict financial risks, and support real-time decision automation while maintaining data integrity and compliance. The study further emphasizes fault-tolerant integration architectures that ensure system reliability, high availability, and seamless interoperability across heterogeneous cloud environments. By combining advanced ML techniques, secure cloud infrastructure, and robust integration patterns, the proposed approach improves risk mitigation, scalability, and resilience in modern financial and enterprise systems
References
1. Brecht, P. (2014). SAP BI in Practice. SAP Press.
2. Kasireddy, J.R. (2025). Quantifying the Causal Effect of FMCSA Enforcement Interventions on Truck Crash Reduction: A Quasi-Experimental Approach Using Carrier-Level Safety Data. International journal of humanities and information technology, 7(2), 25-32
3. Kiran, A., Rubini, P., & Kumar, S. S. (2025). Comprehensive review of privacy, utility and fairness offered by synthetic data. IEEE Access.
4. Subramanian, T., Chinnadurai, N., & Singaram, U. (2025). Performance Investigation on OCF and SCF Study in BLDC Machine Using FTANN Controller. Journal of Electrical Engineering & Technology, 20(4), 2675-2688.
5. Sugumar, R. (2024). AI-Driven Cloud Framework for Real-Time Financial Threat Detection in Digital Banking and SAP Environments. International Journal of Technology, Management and Humanities, 10(04), 165-175.
6. Mallareddi, P. K. D., Keezhadath, A. A., & Kanka, V. (2024). High-Throughput Stream Processing for Global Payment Platforms. American Journal of Data Science and Artificial Intelligence Innovations, 4, 37-73.
7. Chen, H., Chiang, R. H. L., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 36(4), 1165–1188.
8. Natta P K. AI-Driven Decision Intelligence: Optimizing Enterprise Strategy with AI-Augmented Insights[J]. Journal of Computer Science and Technology Studies, 2025, 7(2): 146-152.
9. Meshram, A. K. (2025). Secure and scalable financial intelligence systems using big data analytics in hybrid cloud environments. International Journal of Research and Applied Innovations (IJRAI), 8(6), 13083–13095.
10. Sharma, A., & Joshi, P. (2024). Artificial Intelligence Enabled Predictive Decision Systems for Supply Chain Resilience and Optimization. Journal of Computational Analysis and Applications (JoCAAA), 33(08), 7460–7472. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/4715
11. Gangina, P. (2025). Demystifying Zero-Trust Architecture for Cloud Applications. Journal of Computer Science and Technology Studies, 7(9), 542-548.
12. 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.
13. Mittal, S. (2025). From attribution to action: Causal incrementality and bandit-based optimization for omnichannel customer acquisition in retail media networks. International Journal of Research Publications in Engineering, Technology and Management, 8(6), 13171–13181. https://doi.org/10.15662/IJRPETM.2025.0806021
14. Khokrale, R. (2025). Cybersecurity in ERP-Integrated Supply Chains: Risks and Mitigation Strategies. The Eastasouth Journal of Information System and Computer Science, 3(02), 271-291.
15. Rajasekharan, R. (2025). Optimizing cloud data management through Oracle Database Cloud Engineering. International Journal of Future Innovative Science and Technology (IJFIST), 8(6), 15956–15964.
16. Singh, A. (2025). AI-driven autonomous network control planes for large-scale infrastructure networks. International Journal of Computer Technology and Electronics Communication (IJCTEC), 8(6), 11705–11715. https://doi.org/10.15680/IJCTECE.2025.0806015
17. Genne, S. (2025). Engineering Secure Financial Portals: A Case Study in Credit Line Increase Process Digitization. Journal Of Multidisciplinary, 5(7), 563-570.
18. Kesavan, E. (2023). Assessing laptop performance: A comprehensive evaluation and analysis. Recent Trends in Management and Commerce, 4(2), 175–185. https://doi.org/10.46632/rmc/4/2/22
19. Joseph, J. (2025). Enabling Responsible, Secure and Sustainable Healthcare AI-A Strategic Framework for Clinical and Operational Impact. arXiv preprint arXiv:2510.15943.
20. Kalabhavi, V. (2025). Integrating Trade Promotion Management With SAP CRM For Enhanced Brand Spend Optimization: A Case Study In The Consumer-Packaged Goods Industry. Frontiers in Emerging Artificial Intelligence and Machine Learning, 2(09), 17-22.
21. Poornachandar, T., Latha, A., Nisha, K., Revathi, K., & Sathishkumar, V. E. (2025, September). Cloud-Based Extreme Learning Machines for Mining Waste Detoxification Efficiency. In 2025 4th International Conference on Innovative Mechanisms for Industry Applications (ICIMIA) (pp. 1348-1353). IEEE.
22. Chivukula, V. (2024). The Role of Adstock and Saturation Curves in Marketing Mix Models: Implications for Accuracy and Decision-Making.. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 7(2), 10002–10007.
23. Kusumba, S. (2025). Empowering Federal Efficiency: Building an Integrated Maintenance Management System (Imms) Data Warehouse for Holistic Financial And Operational Intelligence. Journal Of Multidisciplinary, 5(7), 377-384.
24. Panda, M. R., Devi, C., & Dhanorkar, T. (2024). Generative AI-Driven Simulation for Post-Merger Banking Data Integration. Journal of Artificial Intelligence General science (JAIGS) ISSN: 3006-4023, 7(01), 339-350.
25. Sriramoju, S. (2024). Designing scalable and fault-tolerant architectures for cloud-based integration platforms. International Journal of Future Innovative Science and Technology (IJFIST), 7(6), 13839–13851.
26. Karnam, A. (2025). Rolling Upgrades, Zero Downtime: Modernizing SAP Infrastructure with Intelligent Automation. International Journal of Engineering & Extended Technologies Research, 7(6), 11036–11045. https://doi.org/10.15662/IJEETR.2025.0706022
27. Chivukula, V. (2024). The Role of Adstock and Saturation Curves in Marketing Mix Models: Implications for Accuracy and Decision-Making. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 7(2), 10002-10007.
28. 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.
29. Ferdousi, J., Shokran, M., & Islam, M. S. (2026). Designing Human–AI Collaborative Decision Analytics Frameworks to Enhance Managerial Judgment and Organizational Performance. Journal of Business and Management Studies, 8(1), 01-19.
30. 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.
31. Navandar, P. (2022). SMART: Security Model Adversarial Risk-based Tool. International Journal of Research and Applied Innovations, 5(2), 6741-6752.
