Journal article 12 views
Class imbalance Bayesian model averaging for consumer loan default prediction: The role of soft credit information
,
Miao Zhu,
Mike Buckle,
Petr Hajek ,
Mohammad Abedin
Research in International Business and Finance, Volume: 74, Start page: 102722
Swansea University Author:
Mohammad Abedin
Full text not available from this repository: check for access using links below.
DOI (Published version): 10.1016/j.ribaf.2024.102722
Abstract
This study investigates the predictive value of soft information for consumer loan defaults. We propose a novel framework to address class imbalance by utilizing the concept of Bayesian model averaging. Specifically, we assign unequal weights to machine learning sub-models that incorporate different...
| Published in: | Research in International Business and Finance |
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| ISSN: | 0275-5319 |
| Published: |
Elsevier BV
2025
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| Online Access: |
Check full text
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa68662 |
| Abstract: |
This study investigates the predictive value of soft information for consumer loan defaults. We propose a novel framework to address class imbalance by utilizing the concept of Bayesian model averaging. Specifically, we assign unequal weights to machine learning sub-models that incorporate different combinations of variables, thereby creating an accurate and robust model for predicting consumer loan defaults. Additionally, this framework incorporates the Shapley additive explanations (SHAP) method to estimate individual contributions and employs the Bayesian information criterion to assess the variable contributions of the sub-models. We validate the effectiveness and robustness of our proposed method using authentic loan data and publicly available credit default records from a prominent consumer platform in China. Our empirical research suggests that the characteristics of user online behavior are significantly predictive of loan defaults, demonstrating asymmetry at different stages of default. |
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| Keywords: |
Consumer loan, Soft credit information, Class imbalance, Bayesian model averaging, Variable contribution |
| College: |
Faculty of Humanities and Social Sciences |
| Start Page: |
102722 |