Journal of Credit Risk
ISSN:
1744-6619 (print)
1755-9723 (online)
Editor-in-chief: Linda Allen and Jens Hilscher
Generalized additive modeling of the credit risk of Korean personal bank loans
Need to know
- GAM approach with B-spline smoother is very useful for model selection, and yields easily interpretable results.
- Both low-income and high-income borrowers have a high default probability.
- GAMs tend to fit the data better than less flexible models both in-sample and out-of-sample.
- GAMs useful for reducing total misclassification costs.
Abstract
We analyze consumer defaults in a sample of 64 000 customers taking personal loans from a Korean bank. Applying a generalized additive modeling (GAM) framework, we show a nonlinear impact of loan and borrower characteristics. In particular, the likelihood of default is high for both low-income borrowers and high-income borrowers. Our results are robust to a range of different tests, and they highlight the usefulness of the GAM framework, especially the graphical presentation of nonlinearities.
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Copyright Infopro Digital Limited. All rights reserved.
You may share this content using our article tools. Copying this content is for the sole use of the Authorised User (named subscriber), as outlined in our terms and conditions - https://www.infopro-insight.com/terms-conditions/insight-subscriptions/
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