Integrated Cloud and Artificial Intelligence Solutions for Smart Governance Fraud Detection and Digital Service Delivery
DOI:
https://doi.org/10.15680/IJCTECE.2026.0903013Keywords:
Cloud Computing, Artificial Intelligence, Smart Governance, Fraud Detection, Digital Service Delivery, Machine Learning, Predictive Analytics, Cloud-Native Architecture, Cybersecurity, Intelligent Automation, Risk Management, Digital Transformation, Data Governance, High AvailabilityAbstract
The rapid advancement of cloud computing and artificial intelligence (AI) has transformed the way governments and public organizations deliver digital services, manage resources, and protect critical information assets. Integrated cloud and AI solutions enable scalable, secure, and intelligent platforms that enhance operational efficiency, improve decision-making, and provide citizen-centric services. This paper presents a comprehensive framework for integrating cloud-native technologies with AI-driven analytics to support smart governance, fraud detection, and digital service delivery. The proposed approach combines machine learning, predictive analytics, automated workflow orchestration, and cloud-based data management to identify fraudulent activities, optimize public service operations, and strengthen cybersecurity. AI-powered anomaly detection and risk assessment facilitate proactive monitoring of financial transactions, identity verification, and compliance management, thereby reducing operational risks and enhancing transparency. Furthermore, cloud-native architectures improve scalability, high availability, disaster recovery, and interoperability across government departments and enterprise applications. The framework also incorporates secure data governance, privacy protection, and intelligent automation to ensure regulatory compliance while delivering reliable and responsive digital services. By integrating AI with cloud infrastructure, organizations can accelerate digital transformation, improve service quality, and establish resilient governance ecosystems capable of addressing evolving technological and security challenges. The proposed model provides a practical roadmap for developing secure, intelligent, and sustainable digital platforms that support modern governance and enterprise innovation.
References
1. Adlermann, J. F. (2024). A GRA-enhanced cloud AI framework for petabyte-scale multi-tenant environments: Multivariate classification for credit card fraud detection and adaptive risk analytics. International Journal of Engineering & Extended Technologies Research, 6(6), 9075–9083.
2. Damarched, M. K. (2025). Data Governance Challenges in ITSM Platform Transitions. International Journal of Computer Technology and Electronics Communication, 8(6), 11881-11890.
3. Yatam, S. N. K. (2025). Autonomous DevOps: The ZTI-MDS Integration Framework. Journal of Computer Science and Technology Studies, 7(7), 755-763.
4. Suddala, V. R. A. K. (2026). The Rise of Domain-Specific AI Transforming Key Sectors. International Journal of Science, Research and Technology, 9(2), 373-381.
5. Anumula, S. K., & Tatavarthy, K. (2025, July). Balancing Innovation and Ethics: Navigating the Promise and Perils of Algorithmic Solutions in Humanitarian Innovation. In Networking International Conference on Emerging Trends in Expert Applications and Security (pp. 308-319). Cham: Springer Nature Switzerland.
6. Gopisetty, S. (2025). The Babelfish for cloud policies: Using AI to harmonize zero-trust rules across banking microservices. International Journal of Artificial Intelligence and Cloud Computing, 3(2), 1–17. https://doi.org/10.34218/IJAICC_03_02_001
7. Polamreddy, V. R. (2025). Architecting Financially Compliant Enterprise Point-of-Sale Systems: Data Integrity and Revenue Recognition at Scale. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 8(5), 12993-13104.
8. Ambalakannu, M. (2026). Domain-AI Governance Unifying Enterprise AI Adoption and Data Quality Frameworks. International Journal of Science, Research and Technology, 9(2), 444-452.
9. Manda, P. (2025). Disaster recovery by design: Building resilient Oracle database systems in cloud and hyperconverged environments. International Journal of Research and Applied Innovations, 8(4), 12568-12579.
10. Singh, A. (2025). Wi-Fi 8 as a deterministic wireless platform for real-time and mission-critical applications. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 8(4), 12438-12447.
11. Gowda, M. K. S. (2026). Automated Loan Document Analysis and Risk Forecasting Using NLP and Predictive Analytics.
12. Makkena, B. (2025, December). Improving IoT Network Security with a Hybrid Model for IDS in Cloud Infrastructure. In 2025 IEEE Pune Section International Conference (PuneCon) (pp. 1-6). IEEE.
13. Sharma, K., Konudula, J., Srinivas, S., & Mamadiyarov, Z. (2025, August). Leveraging AI and ML to Customize Salesforce CRM for Industry-Specific Solutions. In 2025 International Conference on Intelligent and Secure Engineering Solutions (CISES) (pp. 1492-1497). IEEE.
14. Katta, T. B. (2025, April). AI-Enhanced Orchestration in Hybrid Cloud Enterprise Integration: Transforming Enterprise Data Flows. In International Conference of Global Innovations and Solutions (pp. 118-129). Cham: Springer Nature Switzerland.
15. Kotla, M. R. T. (2025). Enterprise integration lessons from four digital frontlines: A comparative analysis of modern IT ecosystems. International Journal of Research Publications in Engineering, Technology and Management, 8(3), 32–42.
16. Indurthy, V. S. K. (2025). Phased Migration Strategies for Modernizing Enterprise Data Warehouses. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 8(3), 12170-12178.
17. Ambati, K. C. (2026). Unified Supply Chain Intelligence Data, AI, Cloud, and Operations Synergy. International Journal of Science, Research and Technology, 9(2), 391-398.
18. Parasa, M. (2025). Creating hyper-personalized learning journeys using AI in SAP SuccessFactors LMS for individual development and business alignment. International Research Journal of Engineering & Applied Sciences, 13(4), 241–255. https://doi.org/10.55083/irjeas.2025.v13i04022
19. Pothuri, M. K. Building a Seamless Healthcare Data Fabric: Zero-Touch Integration and Scalable Mapping Across Provider, Claims, Recipient, and Pharmacy Source Systems for State Medicaid. IJLRP-International Journal of Leading Research Publication, 6(8).
20. Bheemisetty, N. (2026). Next-Gen Data Ecosystems: Domain-AI across Spark, ETL, and Batch Intelligence. International Journal of Science, Research and Technology, 9(2), 382-390.
21. Holmgren, E. D. L. (2024). AI-augmented fraud detection in cloud platforms: GRA-based risk ranking with cybersecurity and threat prevention for SAP HANA healthcare ERP. International Journal of Advanced Research in Computer Science & Technology, 7(5), 10966–10973.
22. Suddala, V. R. A. K. (2025). Healthcare e-commerce platforms driving secure, scalable, and auditable service delivery. International Journal of Engineering & Extended Technologies Research (IJEETR), 7(1), 9340–9351.
23. Gandhi, S. T. (2024). Enhancing Software Security with AI-Powered SDKs: A Framework for Proactive Threat Mitigation. International Journal of Computer Technology and Electronics Communication, 7(2), 8507-8514.
24. Upadhyay, H. (2026). Agentic AI orchestration frameworks for composable commerce ecosystems: A case study of enterprise transformation. American Journal of Technology, 5(1), 40-54.
25. Beeram, S. (2026). Multi-Cloud Governance with Azure Arc and Lighthouse. International Journal of AI, BigData, Computational and Management Studies, 7(1), 170-172.
26. Grandhe, K. (2026, February). Explainable AI for Predicting SME Loan Defaults Using XGBoost and SHAP. In SoutheastCon 2026 (pp. 1-7). IEEE.
27. Schmitt, M. (2023). Securing the digital world: Protecting smart infrastructures and digital industries with Artificial Intelligence-enabled malware and intrusion detection. arXiv.
28. Gowda, M. K. S. (2024). Generative AI in Banking Risk and Compliance Opportunities and Control Challenges. International Journal of Future Innovative Science and Technology (IJFIST), 7(6), 13946.
29. Silbermann, F. D. (2024). Cloud-native AI and deep learning models for real-time fraud detection and cybersecurity in financial institutions. International Journal of Advanced Research in Computer Science & Technology.
30. Panda, S. S. (2025). Redefining cloud-native performance: A technical evaluation of Microsoft Azure’s Cobalt 100 ARM-based virtual machines. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 8(2), 11815–11830.
31. Sorensen, J. M. (2024). A scalable AI cloud architecture advancing healthcare governance risk oversight and digital trust with machine learning. International Journal of Engineering & Extended Technologies Research.
32. Turner, O. P. W. (2024). Intelligent fraud detection in cloud computing using AI-enabled machine learning and network security analytics. International Journal of Advanced Research in Computer Science & Technology.
33. Zhang, C. J., Gill, A. Q., Liu, B., & Anwar, M. J. (2025). AI-based identity fraud detection: A systematic review. arXiv.
