Robust integration of blockchain and explainable federated learning for automated credit scoring
Journal article
Jovanovic, Zorka, Hou, Zhe, Biswas, Kamanashis and Muthukkumarasamy, Vallipuram. (2024). Robust integration of blockchain and explainable federated learning for automated credit scoring. Computer Networks. 243, pp. 1-16. https://doi.org/10.1016/j.comnet.2024.110303
Authors | Jovanovic, Zorka, Hou, Zhe, Biswas, Kamanashis and Muthukkumarasamy, Vallipuram |
---|---|
Abstract | This article examines the integration of blockchain, eXplainable Artificial Intelligence (XAI), especially in the context of federated learning, for credit scoring in financial sectors to improve the credit assessment process. Research shows that integration of these cutting-edge technologies is in its infancy, specifically in the areas of embracing broader data, model verification, behavioural reliability and model explainability for intelligent credit assessment. The conventional credit risk assessment process utilises historical application data. However, reliable and dynamic transactional customer data are necessary for robust credit risk evaluation in practice. Therefore, this research proposes a framework for integrating blockchain and XAI to enable automated credit decisions. The main focus is on effectively integrating multi-party, privacy-preserving decentralised learning models with blockchain technology to provide reliability, transparency, and explainability. The proposed framework can be a foundation for integrating technological solutions while ensuring model verification, behavioural reliability, and model explainability for intelligent credit assessment. |
Keywords | Automated credit scoring; Blockchain ; Explainable artificial intelligence; Decentralised federated learning |
Year | 01 Jan 2024 |
Journal | Computer Networks |
Journal citation | 243, pp. 1-16 |
Publisher | Elsevier B.V. (Netherlands) |
ISSN | 1389-1286 |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.comnet.2024.110303 |
Web address (URL) | https://www.sciencedirect.com/science/article/pii/S138912862400135X?via%3Dihub |
Open access | Open access |
Research or scholarly | Research |
Page range | 1-16 |
Publisher's version | License File Access Level Open |
Output status | Published |
Publication dates | |
Online | 07 Mar 2024 |
Publication process dates | |
Accepted | 01 Mar 2024 |
Deposited | 26 Sep 2024 |
Additional information | © 2024 The Authors. Published by Elsevier B.V |
This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). | |
Place of publication | Netherlands |
https://acuresearchbank.acu.edu.au/item/90yv8/robust-integration-of-blockchain-and-explainable-federated-learning-for-automated-credit-scoring
Download files
Publisher's version
OA_Biswas_2024_Robust_integration_of_blockchain_and_explainable.pdf | |
License: CC BY 4.0 | |
File access level: Open |
12
total views6
total downloads0
views this month1
downloads this month