AI-Driven Fraud Detection via Agile Cloud Migration Deep Neural Networks, RiskPredict360 Analytics, and SAP HANA ERP Integration

Antoine Pierre DeschampsLaroche

doi:10.15662/IJRAI.2023.0601005

Authors

Antoine Pierre DeschampsLaroche Cloud Security Engineer, France Author

DOI:

https://doi.org/10.15662/IJRAI.2023.0601005

Keywords:

AI-driven fraud detection, Agile cloud migration, Deep neural networks, RiskPredict360 analytics, SAP HANA, ERP integration, Real-time monitoring, Financial cybersecurity, Predictive analytics, Anomaly detection, Threat intelligence, Machine learning, Cloud-based ERP, Scalable architecture, Enterprise risk management

Abstract

The increasing sophistication of financial fraud requires advanced detection mechanisms that combine real-time analytics, scalable infrastructure, and intelligent algorithms. This paper presents an AI-driven fraud detection framework implemented through agile cloud migration, integrating deep neural networks, RiskPredict360 analytics, and SAP HANA ERP systems. The framework leverages deep learning models to identify anomalous transaction patterns, while RiskPredict360 provides self-service analytics for financial analysts to gain actionable insights without extensive technical expertise. SAP HANA–powered cloud infrastructure ensures high-speed data processing, secure storage, and seamless integration with ERP modules, facilitating real-time monitoring and automated threat response. Agile cloud migration enables scalable deployment across enterprise environments, ensuring resilience, adaptability, and operational efficiency. Experimental results demonstrate the framework’s effectiveness in improving fraud detection accuracy, reducing false positives, and strengthening enterprise cybersecurity posture, offering a comprehensive solution for modern financial operations.

References

1. Partridge, C. (1996). BORO: Business Objects Reference Ontology. (Used as a semantic modeling foundation.) Wikipedia

2. Raj, A. A., &Sugumar, R. (2022, December). Monitoring of the Social Distance between Passengers in Real-time through Video Analytics and Deep Learning in Railway Stations for Developing the Highest Efficiency. In 2022 International Conference on Data Science, Agents & Artificial Intelligence (ICDSAAI) (Vol. 1, pp. 1-7). IEEE.

3. Usha, G., Babu, M. R., & Kumar, S. S. (2017). Dynamic anomaly detection using cross layer security in MANET. Computers & Electrical Engineering, 59, 231-241.

4. Kumar, R., Al-Turjman, F., Anand, L., Kumar, A., Magesh, S., Vengatesan, K., ...& Rajesh, M. (2021). Genomic sequence analysis of lung infections using artificial intelligence technique. Interdisciplinary Sciences: Computational Life Sciences, 13(2), 192-200.

5. 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.

6. Ramakrishna, S. (2022). AI-augmented cloud performance metrics with integrated caching and transaction analytics for superior project monitoring and quality assurance. International Journal of Engineering & Extended Technologies Research (IJEETR), 4(6), 5647–5655. https://doi.org/10.15662/IJEETR.2022.0406005

7. Thangavelu, K., Sethuraman, S., &Hasenkhan, F. (2021). AI-Driven Network Security in Financial Markets: Ensuring 100% Uptime for Stock Exchange Transactions. American Journal of Autonomous Systems and Robotics Engineering, 1, 100-130.

8. Li, H., Meng, D., Wang, H., & Li, X. (2020). Knowledge Federation: A Unified and Hierarchical Privacy-Preserving AI Framework. arXiv preprint. arXiv

9. Nasirigerdeh, R., Torkzadehmahani, R., Matschinske, J., Baumbach, J., Rueckert, D., &Kaissis, G. (2021). HyFed: A Hybrid Federated Framework for Privacy-preserving Machine Learning. arXiv preprint. arXiv

10. Kalra, S., Wen, J., Cresswell, J. C., Volkovs, M., & Tizhoosh, H. R. (2021). Decentralized Federated Learning through Proxy Model Sharing (ProxyFL). arXiv preprint. arXiv

11. Truong, N., Nguyen, V. C., & Phua, C. (2021). Privacy preservation in federated learning: an insightful survey. Computer Science Review. (Surveying privacy-preserving techniques in FL in relation to GDPR.) ScienceDirect

12. ISO/IEC 19831:2015. (2015). Cloud Infrastructure Management Interface (CIMI) Model and RESTful HTTP based Protocol. ISO. ISO

13. DMTF. (2016). DMTF’s Cloud Infrastructure Management Interface (CIMI) 2.0. DMTF. dmtf.org

14. Kumar, R. K. (2022). AI-driven secure cloud workspaces for strengthening coordination and safety compliance in distributed project teams. International Journal of Research and Applied Innovations (IJRAI), 5(6), 8075–8084. https://doi.org/10.15662/IJRAI.2022.0506017

15. Sivaraju, P. S. (2022). Enterprise-Scale Data Center Migration and Consolidation: Private Bank's Strategic Transition to HP Infrastructure. International Journal of Computer Technology and Electronics Communication, 5(6), 6123-6134.

16. Pachyappan, R., Vijayaboopathy, V., & Paul, D. (2022). Enhanced Security and Scalability in Cloud Architectures Using AWS KMS and Lambda Authorizers: A Novel Framework. Newark Journal of Human-Centric AI and Robotics Interaction, 2, 87-119.

17. Pichaimani, T., Gahlot, S., &Ratnala, A. K. (2022). Optimizing Insurance Claims Processing with Agile-LEAN Hybrid Models and Machine Learning Algorithms. American Journal of Autonomous Systems and Robotics Engineering, 2, 73-109.

18. Kumar, S. N. P. (2022). Improving Fraud Detection in Credit Card Transactions Using Autoencoders and Deep Neural Networks (Doctoral dissertation, The George Washington University).

19. Muthusamy, M. (2022). AI-Enhanced DevSecOps architecture for cloud-native banking secure distributed systems with deep neural networks and automated risk analytics. International Journal of Research Publication and Engineering Technology Management, 6(1), 7807–7813. https://doi.org/10.15662/IJRPETM.2022.0506014

20. Singh, H. (2025). AI-Powered Chatbots Transforming Customer Support through Personalized and Automated Interactions. Available at SSRN 5267858.

21. Arora, Anuj. "The Significance and Role of AI in Improving Cloud Security Posture for Modern Enterprises." International Journal of Current Engineering and Scientific Research (IJCESR), vol. 5, no. 5, 2018, ISSN 2393-8374 (Print), 2394-0697 (Online).

22. Althati, C., Krothapalli, B., Konidena, B. K., &Konidena, B. K. (2021). Machine learning solutions for data migration to cloud: Addressing complexity, security, and performance. Australian Journal of Machine Learning Research & Applications, 1(2), 38-79.

23. Nagarajan, G. (2022). Optimizing project resource allocation through a caching-enhanced cloud AI decision support system. International Journal of Computer Technology and Electronics Communication, 5(2), 4812–4820. https://doi.org/10.15680/IJCTECE.2022.0502003

24. Gonepally, S., Amuda, K. K., Kumbum, P. K., Adari, V. K., &Chunduru, V. K. (2022). Teaching software engineering by means of computer game development: Challenges and opportunities using the PROMETHEE method. SOJ Materials Science & Engineering, 9(1), 1–9.

25. Anand, L., &Neelanarayanan, V. (2019). Feature Selection for Liver Disease using Particle Swarm Optimization Algorithm. International Journal of Recent Technology and Engineering (IJRTE), 8(3), 6434-6439.

26. Mani, R. (2022). Enhancing SAP HANA Resilience and Performance on RHEL using Pacemaker: A Strategic Approach to Migration Optimization and Dual-Function Infrastructure Design. International Journal of Computer Technology and Electronics Communication, 5(6), 6061-6074.

27. Raj, A. A., &Sugumar, R. (2022, December). Monitoring of the Social Distance between Passengers in Real-time through Video Analytics and Deep Learning in Railway Stations for Developing the Highest Efficiency. In 2022 International Conference on Data Science, Agents & Artificial Intelligence (ICDSAAI) (Vol. 1, pp. 1-7). IEEE.

28. Navandar, Pavan. "Enhancing Cybersecurity in Airline Operations through ERP Integration: A Comprehensive Approach." Journal of Scientific and Engineering Research 5, no. 4 (2018): 457-462.

29. Selvi, R., Saravan Kumar, S., & Suresh, A. (2014). An intelligent intrusion detection system using average manhattan distance-based decision tree. In Artificial Intelligence and Evolutionary Algorithms in Engineering Systems: Proceedings of ICAEES 2014, Volume 1 (pp. 205-212). New Delhi: Springer India.

30. CGAP. (2021). Interoperability in Digital Financial Services: Technical Note. CGAP. CGAP