Developing Explainable AI Enabled Edge Cloud Framework for Financial Analytics and Intelligent Risk Governance

Ifesinachi Aroh; Ifesinachi Aroh

doi:10.15662/IJSRAT.2025.0806019

Developing Explainable AI Enabled Edge Cloud Framework for Financial Analytics and Intelligent Risk Governance

Ifesinachi Aroh

Independent Researcher, USA

Abstract

The convergence of edge computing and artificial intelligence is reshaping financial analytics by enabling ultra-low latency processing and decentralized decision-making. However, deploying complex deep learning models across edge-cloud environments introduces significant governance challenges, notably the opaque "black box" nature of advanced algorithms and strict regulatory mandates for algorithmic auditability. This paper proposes a unified, Explainable AI (XAI)-enabled edge-cloud framework tailored for real-time financial analytics and adaptive risk governance. By distributing computational workloads across edge nodes for local feature extraction and centralized cloud infrastructure for macro-risk modeling, the framework maintains high system throughput while minimizing bandwidth overhead. Integrated XAI modules—combining ante-hoc interpretable architectures and post-hoc attribution methods—generate real-time, human-understandable explanations for algorithmic decisions like fraud flags and credit evaluations. Furthermore, a continuous risk governance engine continuously tracks model drift, algorithmic bias, and regulatory compliance across the edge network. Empirical evaluations demonstrate that this architecture achieves sub-15 millisecond inference latencies, lowers data transmission costs by 38%, and maintains high compliance visibility. Ultimately, this framework establishes a trusted blueprint for financial institutions seeking to balance operational agility with rigorous risk controls.

Article Information

Journal	International Journal of Science, Research and Technology
Volume (Issue)	Vol. 8 No. 6 (2025): International Journal of Science, Research and Technology (IJSRAT)
DOI	https://doi.org/10.15662/IJSRAT.2025.0806019
Pages	15431-15439
Published	December 19, 2025
Copyright	All rights reserved
Open Access	This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite	Ifesinachi Aroh (%2025). Developing Explainable AI Enabled Edge Cloud Framework for Financial Analytics and Intelligent Risk Governance. International Journal of Science, Research and Technology , Vol. 8 No. 6 (2025): International Journal of Science, Research and Technology (IJSRAT) , pp. 15431-15439. https://doi.org/10.15662/IJSRAT.2025.0806019

References

1. Kanchumarthi, S. N. V. P. (2024, April). Hybrid network security architecture: F5–AWS integration, zero-trust enforcement, and SD-WAN for PCI DSS-compliant hybrid environments. World Journal of Advanced Research and Reviews, 22(1), 2111–2117. https://doi.org/10.30574/wjarr.2024.22.1.1162
2. Mohammed, S. (2023). Modernizing enterprise service desk and EUC operations with AI-powered automation. International Journal of Innovative Research in Computer and Communication Engineering, 11(12), 12235–12244. https://doi.org/10.15680/IJIRCCE.2023.1112044
3. Gurram, S. K. (2025). Revolutionizing financial infrastructure: the convergence of blockchain and cloud in next-generation payment networks. Journal of Computer Science and Technology Studies, 7(4), 607-618.
4. Alex Roney Mathew. (2019). Malware analysis of API calls using FPGA hardware level security. International Journal for Research in Applied Science & Engineering Technology, 7(3), 898–900.
5. Rohit Wadhwa. (2024). A Cloud-Native Approach to Enterprise Systems Engineering Using Event-Driven Microservices and Distributed Databases. International Journal of Cloud Computing (IJCC), 2(1), 81–93. DOI: https://doi.org/10.34218/IJCC_02_01_005
6. Anumula, S. K., Talluru, N., Gangavarapu, R., Gupta, S., & Tatavarthy, K. Quantum-Inspired Computing: Classical Approaches to Machine Learning. In Quantum-Inspired Neural Networks (pp. 74-88). CRC Press.
7. Narayanan, S. (2022). Transforming cybersecurity with AI-driven dashboards: A cloud-native implementation framework for real-time threat detection and automated response. International Journal of Future Innovative Science and Technology (IJFIST), 5(5), 9217.
8. Meesala, L. K. (2023). Modern security information and event management: Architecture, analytics pipelines, and empirical evaluation of SOC-scale threat detection. International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN, 2456-3307.
9. Meesala, A. (2023). Real-time stock price reconciliation in cloud-native streaming architectures: A reinforcement learning framework. World Journal of Advanced Research and Reviews, 19(2), 1747-1755.
10. Vas, M. R. (2025). AI-Augmented DevSecOps for Cloud Environments: Bridging Security Gaps in Modern Continuous Delivery Pipelines. International Journal of Computer Technology and Electronics Communication, 8(6), 11908-11917.
11. Kale, P. (2024). AI-Augmented DevSecOps Pipelines for Secure and Efficient Software Delivery in Cloud-Native Platforms. International Journal of Emerging Research in Engineering and Technology, 5(3), 201-209.
12. Venkiteela, P. (2025). Comparative analysis of leading API management platforms for enterprise API modernization. International Journal of Computer Applications, 187(54), 35-48.
13. Sojol, J. I., Shihab, M. H., Ahamed, T., Siam, M. A. S., & Siddique, S. (2019, February). Nirob: a next generation green earth technology based innovative system for metropolitan sound pollution management. In 2019 21st international conference on advanced communication technology (ICACT) (pp. 722-727). IEEE.
14. Mudusu, S. K. (2025). Data Engineering Challenges in AI-Driven Healthcare IT Systems: Navigating Real-Time Analytics and Interoperability.
15. Hossain, M. S., Ali, M., & Haider, P. S. (2022). Generative AI and Predictive Business Analytics for Early Detection of Cybersecurity Threats in Enterprise Networks. American Journal of Economics and Business Management, 5(12).
16. Dasari, H. (2025, October). Site reliability engineering practices for error budget management in large-scale systems. International Journal of Applied Mathematics, 38(5s), 991–1001.
17. Somu, B. (2022). Modernizing core banking infrastructure: The role of AI/ML in transforming IT services. Mathematical Statistician and Engineering Applications, 71(4), 16928–16960.
18. Billah, M. M., Nath, A. D., Das, D., Mahmud, T., & Rahman, R. (2023). Skin Cancer Classification using NasNet. World J. Adv. Res. Rev, 19, 1652-1658.
19. Potdar, A., Kodela, V., Srinivasagopalan, L. N., Khan, I., Chandramohan, S., & Gottipalli, D. (2025, July). Next-Generation Autonomous Troubleshooting Using Generative AI in Heterogeneous Cloud Systems. In 2025 International Conference on Information, Implementation, and Innovation in Technology (I2ITCON) (pp. 1-7). IEEE.
20. Sudhan, S. K. H. H., & Kumar, S. S. (2016). Gallant Use of Cloud by a Novel Framework of Encrypted Biometric Authentication and Multi Level Data Protection. Indian Journal of Science and Technology, 9, 44.
21. Veershetty, G. (2024). Robust Transition Management From Implementation to Application Management Services. Available at SSRN 6890119.
22. Rahaman, M. M., Biswas, B., & Hossain, M. S. (2022). Dynamic task prioritization in Meta-GNN (graph neural networks) for fraud detection: A meta-reinforcement learning approach with adaptive graph sparsification. International Journal of Science and Engineering Research, 5(1), 207-221.
23. Singh, I. K. (2024). DevSecOps for enterprise ontology platforms: Continuous validation, security, and compliance of semantic knowledge systems. International Journal of Research Publications in Engineering, Technology and Management, 7(2), 10359–10369.
24. Onik, T. A., Irin, K. N., Azam, M. N., Akter, K. S., Nabil, M. A., Hossain, I., & Akter, S. (2023). Artificial Intelligence-Driven Early Detection of Neurological Disorders and Its Implications for Personalized Rehabilitation Strategies. Vascular and Endovascular Review, 6(2), 104-111.
25. Umasankar, P., & Saravanan, K. (2016). Artificial Neural Network based Smart Charging Systems for Lead Acid Batteries. Asian Journal of Research in Social Sciences and Humanities, 6(cs1), 181-191.
26. Anand, L., & Neelanarayanan, V. (2020, October). Enchanced multiclass intrusion detection using supervised learning methods. In AIP conference proceedings (Vol. 2282, No. 1, p. 020044). AIP Publishing LLC.
27. Raja, G. V. (2020). Metadata gets a makeover: The machine learning approach. International Journal of Computer Technology and Electronics Communication, 3(6), 2900-2903.
28. Anbazhagan, R. S. K. (2016). A Proficient Two Level Security Contrivances for Storing Data in Cloud.
29. Sudhan, S. K. H. H., & Kumar, S. S. (2015). An innovative proposal for secure cloud authentication using encrypted biometric authentication scheme. Indian journal of science and technology, 8(35), 1-5.
30. Jayaraman, S., Rajendran, S., & P, S. P. (2019). Fuzzy c-means clustering and elliptic curve cryptography using privacy preserving in cloud. International Journal of Business Intelligence and Data Mining, 15(3), 273-287.
31. Anand, L. (2022). Integrating Kubernetes Microservices with Privileged Access Security and Real-Time Fraud Detection for Modern Enterprise Systems. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(5), 7453-7461.
32. Sugumar, R. (2025). Next-Generation AI Assistance Platforms for Streamlined Consumer Electronics Configuration and Adoption. International Journal of Research and Applied Innovations, 8(5), 13083-13093.
33. Vimal Raja, G. (2022). Leveraging Machine Learning for Real-Time Short-Term Snowfall Forecasting Using MultiSource Atmospheric and Terrain Data Integration. International Journal of Multidisciplinary Research in Science, Engineering and Technology, 5(8), 1336-1339.
34. Soundappan, S. J. (2023). Designing Intelligent Enterprise Platforms Using Machine Learning Driven API Engineering and Cloud Native Security. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(4), 9074-9081.