Journal of Credit Risk

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

Young-Ah Kim, Peter G Moffatt and Simon A Peters

  • 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.

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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