Generalised additive modelling of the credit risk of Korean personal bank loans

Peter Moffatt; Young-Ah Kim; Simon Peters

Generalised additive modelling of the credit risk of Korean personal bank loans

Peter Moffatt, Young-Ah Kim, Simon Peters

Research output: Contribution to journal › Article › peer-review

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 non-linear impact of loan and borrower characteristics. In particular, the likelihood of default is high for both low income borrowers as well as high income borrowers. Our results are robust to a range of different tests, and highlight the usefulness of the GAM framework, especially the graphical presentation of non-linearities.

Original language	English
Journal	Journal of Credit Risk
Publication status	Accepted/In press - 13 Sep 2021

Keywords

Generalised additive models; B-spline; credit scoring; loan defaults; signal detection theory; mis-classification costs

Embargoed Document

Accepted_Manuscript
Accepted author manuscript, 592 KB
Embargo ends: 31/12/99

Cite this

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title = "Generalised additive modelling of the credit risk of Korean personal bank loans",

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 non-linear impact of loan and borrower characteristics. In particular, the likelihood of default is high for both low income borrowers as well as high income borrowers. Our results are robust to a range of different tests, and highlight the usefulness of the GAM framework, especially the graphical presentation of non-linearities.",

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AB - 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 non-linear impact of loan and borrower characteristics. In particular, the likelihood of default is high for both low income borrowers as well as high income borrowers. Our results are robust to a range of different tests, and highlight the usefulness of the GAM framework, especially the graphical presentation of non-linearities.

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