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An FTwNB Shield: A Credit Risk Assessment Model for Data Uncertainty and Privacy Protection

Authors :
Shaona Hua
Chunying Zhang
Guanghui Yang
Jinghong Fu
Zhiwei Yang
Liya Wang
Jing Ren
Source :
Mathematics, Vol 12, Iss 11, p 1695 (2024)
Publication Year :
2024
Publisher :
MDPI AG, 2024.

Abstract

Credit risk assessment is an important process in bank financial risk management. Traditional machine-learning methods cannot solve the problem of data islands and the high error rate of two-way decisions, which is not conducive to banks’ accurate credit risk assessment of users. To this end, this paper establishes a federated three-way decision incremental naive Bayes bank user credit risk assessment model (FTwNB) that supports asymmetric encryption, uses federated learning to break down data barriers between banks, and uses asymmetric encryption to protect data security for federated processes. At the same time, the model combines the three-way decision methods to realize the three-way classification of user credit (good, bad and delayed judgment), so as to avoid the loss of bank interests caused by the forced division of uncertain users. In addition, the model also incorporates incremental learning steps to eliminate training samples with poor data quality to further improve the model performance. This paper takes German Credit data and Default of Credit Card Clients data as examples to conduct simulation experiments. The result shows that the performance of the FTwNB model has been greatly improved, which verifies that it has good credit risk assessment capabilities.

Details

Language :
English
ISSN :
22277390
Volume :
12
Issue :
11
Database :
Directory of Open Access Journals
Journal :
Mathematics
Publication Type :
Academic Journal
Accession number :
edsdoj.f12d5a5367485eab83309e3d6b0154
Document Type :
article
Full Text :
https://doi.org/10.3390/math12111695