Decentralized Banking Network for Secure Predictive Assessment and Cross-Entity Knowledge Sharing
DOI:
https://doi.org/10.37547/ijasr-06-07-04Keywords:
Decentralized banking, blockchain, predictive assessment, distributed ledger technologyAbstract
The rapid transformation of financial systems requires secure and intelligent architectures capable of supporting predictive analysis, decentralized operations, and collaborative knowledge exchange. Traditional banking infrastructures often depend on centralized data management, creating challenges related to privacy risks, limited interoperability, and restricted cross-entity collaboration. This research proposes a Decentralized Banking Network (DBN) designed to integrate blockchain-based distributed systems, predictive assessment mechanisms, and secure knowledge-sharing capabilities.
The proposed framework enables financial institutions to collaboratively analyse data while maintaining ownership and confidentiality of sensitive information. The architecture combines distributed ledger technology, intelligent prediction models, and decentralized governance mechanisms to improve financial decision-making. Blockchain concepts provide transparency and trust among participating entities, while predictive assessment techniques support risk evaluation, fraud detection, and strategic planning.
The theoretical foundation of this research is derived from distributed ledger systems, decentralized control, and federated financial intelligence. Distributed ledger technology provides mechanisms for secure and transparent transactions across independent participants (Sunyaev and Sunyaev, 2020). Recent developments in federated financial ecosystems demonstrate the potential of decentralized analytics for improving risk assessment while maintaining data sovereignty (Arifin Shawn et al., 2025).
The proposed network highlights how decentralized banking models can improve security, collaboration, and predictive accuracy. However, challenges related to scalability, regulatory compliance, computational complexity, and governance remain significant considerations. This research provides a conceptual framework for future banking ecosystems where institutions can achieve secure knowledge sharing without compromising confidential financial information.
References
1. Aave, “Aave Documentation,” Aave, [Online]. Available: https://aave.com/docs [Accessed: Nov. 5, 2024 ].
2. A. Sunyaev and A. Sunyaev, “Distributed ledger technology. Internet computing: Principles of distributed systems and emerging internet-based technologies,” pp. 265–299, 2020.
3. A. Zwitter and J. Hazenberg, “Decentralized network governance: blockchain technology and the future of regulation,” Frontiers in Blockchain, vol. 3, p. 12, 2020.
4. L. Bakule, “Decentralized control: Status and outlook,” Annu. Rev. Control, vol. 38, no. 1, pp. 71–80, 2014.
5. K. K. Goyal, "Scalable Data Lakes for AI Workloads: A Multitenant Architecture for Big Data Orchestration," 2025 IEEE International Conference on Computing (ICOCO), Kuching, Malaysia, 2025, pp. 266-271, doi: 10.1109/ICOCO67189.2025.11334100.
6. M. A. Arifin Shawn, N. Begum Asha, D. S. Jatav and R. Nair, "Federated Cloud Finance Ecosystem for Decentralized Risk Analytics and Sovereign Data Integrity through Cross-Institutional Learning," 2025 IEEE 7th International Conference on Cybernetics, Cognition and Machine Learning Applications (ICCCMLA), Hamburg, Germany, 2025, pp. 1-6, doi: 10.1109/ICCCMLA66092.2025.11580915.
7. M. Mandapuram, “Applications of Blockchain and Distributed Ledger Technology (DLT) in Commercial Settings,” Asian Accounting and Auditing Advancement, vol. 7, no. 1, pp. 50–57, 2016.
8. M. Swan, Blockchain: Blueprint for a New Economy, O’Reilly Media, Inc., 2015.
9. R. Raman, C. Viswanathan, A. Shrirvastava, E. N. Ganesh, and others, “Blockchain based future banking by decentralized exchanges,” in Proc. Int. Mobile, Intelligent, and Ubiquitous Computing Conf. (MIUCC), pp. 367–372, 2023.
10. S. Nakamoto, “Bitcoin: A peer-to-peer electronic cash system,,” 2008.
11. S. Z. Mustafa, A. K. Kar, and M. F. W. H. A. Janssen, “Understanding the impact of digital service failure on users: Integrating Tan’s failure and DeLone and McLean’s success model,” Int. J. Inf. Manag., vol. 53, p. 102119, 2020.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Dr. Alicia Bennett

This work is licensed under a Creative Commons Attribution 4.0 International License.