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
AuthorsJovanovic, 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.

KeywordsAutomated credit scoring; Blockchain ; Explainable artificial intelligence; Decentralised federated learning
Year01 Jan 2024
JournalComputer Networks
Journal citation243, pp. 1-16
PublisherElsevier B.V. (Netherlands)
ISSN1389-1286
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 accessOpen access
Research or scholarlyResearch
Page range1-16
Publisher's version
License
File Access Level
Open
Output statusPublished
Publication dates
Online07 Mar 2024
Publication process dates
Accepted01 Mar 2024
Deposited26 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 publicationNetherlands
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https://acuresearchbank.acu.edu.au/item/90yv8/robust-integration-of-blockchain-and-explainable-federated-learning-for-automated-credit-scoring

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