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Generalized additive modeling of the credit risk of Korean personal bank loans

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  • Young-Ah Kim
  • Peter G Moffatt
  • Simon A Peters

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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Handle: RePEc:rsk:journ1:7953986
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