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Robust integration of blockchain and explainable federated learning for automated credit scoring
Jovanovic, Zorka Hou, Zhe Biswas, Kamanashis Muthukkumarasamy, Vallipuram
Jovanovic, Zorka
Hou, Zhe
Biswas, Kamanashis
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
Date
2024
Type
Journal article
Journal
Book
Volume
243
Issue
Page Range
1-16
Article Number
ACU Department
Peter Faber Business School
Faculty of Law and Business
Faculty of Law and Business
Collections
Relation URI
Event URL
Open Access Status
Published as ‘gold’ (paid) open access
License
CC BY 4.0
File Access
Open
Notes
© 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/).
This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).