Journal of Credit Risk

Risk.net

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.

Sorry, our subscription options are not loading right now

Please try again later. Get in touch with our customer services team if this issue persists.

New to Risk.net? View our subscription options

If you already have an account, please sign in here.

You need to sign in to use this feature. If you don’t have a Risk.net account, please register for a trial.

Sign in
You are currently on corporate access.

To use this feature you will need an individual account. If you have one already please sign in.

Sign in.

Alternatively you can request an individual account here: