Sustainable AI-Driven Architectures for Real-Time Derivative Accounting Across Multi-Counterparty Financial Networks
Abstract
Green AI architecture design for real-time derivative valuation across multilayer multi-counterparty networks that account for systemic and market-specific risk scenarios. A real-time data-pipeline and provenance management approach incorporates user-defined risk metrics that are used for SLA invocation and applied to distributed data streams to establish Near or Real-time apparent price and P&L: Risk vectors are also exploited for scenario generation in supporting and orchestrating stress testing. Provenance and quality parameters control the overall risk of the output data, whilst stress testing scenarios based on the same risk metrics assess the capacity of a Green AI architecture to fulfil the stipulated demands. A supporting process implements data-longevity concepts in SLA-driven regulatory reporting.
The drive towards supporting sustainable computing integrates the practices of the emerging Green AI domain with financial Information Communication Technology (ICT) infrastructures. This ushers in the consideration of Green-Computing concepts and metrics at a more detailed architectural level—directly extending the “Green” and “Minimal-Mid-Point” ICT enablers of Risk-Aware Green AI to include new components. This exploration addresses the Integrated Financial System Integration challenge, in the specific context of Real-time Apparent Pricing. The focus is the real-time accounting of retail derivatives traded over multiple counterparty pairs, where satisfactory performance characteristics need to be maintained across a specified amount of risk-based latency.
Article Information
Journal |
International Journal of Science, Research and Technology |
|---|---|
Volume (Issue) |
Vol. 8 No. 3 (2025): International Journal of Science, Research and Technology (IJSRAT) |
DOI |
|
Pages |
14245-14261 |
Published |
May 7, 2025 |
| Copyright |
All rights reserved |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Vinod Battapothu, Isabella Romano (%2025). Sustainable AI-Driven Architectures for Real-Time Derivative Accounting Across Multi-Counterparty Financial Networks. International Journal of Science, Research and Technology , Vol. 8 No. 3 (2025): International Journal of Science, Research and Technology (IJSRAT) , pp. 14245-14261. https://doi.org/10.15662/IJSRAT.2025.0803005 |
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11. Venkatasubramanian, K., Yasmeen, Z., Reddy Kothapalli Sondinti, L., Valiki, S., Tejpal, S., & Paulraj, K. (2024, November). Unified deep learning framework integrating CNNs and vision transformers for efficient and scalable solutions. In Proceedings of the 3rd International Conference on Optimization Techniques in the Field of Engineering (ICOFE-2024).
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13. Syed, S. (2024). Transforming Manufacturing Plants for Heavy Vehicles: How Data Analytics Supports Planet 2050’s Sustainable Vision. Nanotechnology Perceptions, 20(6), 10-62441.
14. Vijayarajeswari, R., Pushpa, N., Chandra Rao Nampalli, R., Koli, V., & Murali, E. (2024, November). Optimized Deep Learning Techniques for Scalable Transparent and Real-Time Secure IoT Networks. In Proceedings of the 3rd International Conference on Optimization Techniques in the Field of Engineering (ICOFE-2024).
15. Ganti, V. K. A. T. (2024). Designing Bias-Aware AI Models to Address Health Disparities in Multi-Ethnic Healthcare Systems. American Journal of Analytics and Artificial Intelligence (ajaai) with ISSN 3067-283X, 2(1).
16. Vankayalapati, R. K., Yasmeen, Z., Bansal, A., Dileep, V., & Abhireddy, N. (2024, December). Advanced fault detection in semiconductor manufacturing processes using improved AdaBoost RT model. In 2024 9th International Conference on Communication and Electronics Systems (ICCES) (pp. 467-472). IEEE.
17. Maguluri, K. K., Pandugula, C., & Yasmeen, Z. (2024). Neural Network Approaches for Real-Time Detection of Cardiovascular Abnormalities.
18. Nampalli, R. C. R., Syed, S., Bansal, A., Vankayalapati, R. K., & Danda, R. R. (2024, December). Optimizing Automotive Manufacturing Supply Chains with Linear Support Vector Machines. In 2024 9th International Conference on Communication and Electronics Systems (ICCES) (pp. 574-579). IEEE.
19. Pandugula, C., Ganti, V. K. A. T., & Mallesham, G. (2024). Predictive Modeling in Assessing the Efficacy of Precision Medicine Protocols. EDUCATIONAL ADMINISTRATION: THEORY AND PRACTICE Учредители: Green Publication БИБЛИОМЕТРИЧЕСКИЕ ПОКАЗАТЕЛИ: Входит в РИНЦ: на рассмотрении Цитирований в РИНЦ: 0 Входит в ядро РИНЦ: нет Цитирований из ядра РИНЦ: 0 Рецензии: нет данных Процентиль журнала в рейтинге SI: ТЕМАТИЧЕСКИЕ НАПРАВЛЕНИЯ:.
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21. Krishna AzithTejaGanti, V., Senthilkumar, K. P., Robinson L, T., Karunakaran, S., Pandugula, C., & Khatana, K. (2024, November). Energy-Efficient Real-Time Hybrid Deep Learning Framework for Adaptive Iot Intrusion Detection with Scalable and Dynamic Threat Mitigation. In Proceedings of the 3rd International Conference on Optimization Techniques in the Field of Engineering (ICOFE-2024).
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23. Azith Teja Ganti, V. K., Maguluri, K. K., & Sudha Rani, P. R. (2024). Neural Network Applications in Understanding Neurodegenerative Disease Progression. Frontiers in Health Informatics, 13(8), 471.
24. Pamisetty, A. (2024). Integration Of Artificial Intelligence And Machine Learning In National Food Service Distribution Networks.
25. Sriram, H. K., Challa, S. R., Challa, K., & ADUSUPALLI, B. (2024). Strategic Financial Growth: Strengthening Investment Management. Secure Transactions, and Risk Protection in the Digital Era. Secure Transactions, and Risk Protection in the Digital Era (November 10, 2024).
26. Sriram, H. K. (2024). Leveraging AI and machine learning for enhancing secure payment processing: A study on generative AI applications in real-time fraud detection and prevention. Available at SSRN 5203586.
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30. Moreno T., J. F., Zapata, C., & Aragon, D. (2024). Neural networks and the nonlinear Feynman–Kac theorem applied to financial options pricing. SN Computer Science, 5, 697.
31. Patterson, D., Gonzalez, J., Le, Q., Liang, C., Munguia, L.-M., Rothchild, D., So, D., Texier, M., & Dean, J. (2021). Carbon emissions and large neural network training. Communications of the ACM, 64(11), 39–47.
32. Kannan, S., & Seenu, A. (2024). Advancing Sustainability Goals with AI Neural Networks: A Study on Machine Learning Integration for Resource Optimization and Environmental Impact. management, 32(2).
33. Kannan, S. (2024). Revolutionizing Agricultural Efficiency: Leveraging AI Neural Networks and Generative AI for Precision Farming and Sustainable Resource Management. UtilitasMathematica.
34. Recharla, M., Chava, K., Chakilam, C., & Suura, S. R. (2024). Postpartum Depression: Molecular Insights and AI-Augmented Screening Techniques for Early Intervention. International Journal of Medical Toxicology and Legal Medicine, 27(5), 935-957.
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