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Volume 61, Issue 4

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Does Non-linearity Matter in Retail Credit Risk Modeling?

Jagric, Timotej; Jagric, Vita; Kracun, Davorin

Year: 2011   Volume: 61   Issue: 4   Pages: 384-402

Abstract: In this research we propose a new method for retail credit risk modeling. In order to capture possible non-linear relationships between credit risk and explanatory variables, we use a learning vector quantization (LVQ) neural network. The model was estimated on a dataset from Slovenian banking sector. The proposed model outperformed the benchmarking (LOGIT) models, which represent the standard approach in banks. The results also demonstrate that the LVQ model is better able to handle the properties of categorical variables.

JEL classification: C45, C25, D81, G21

Keywords: retail banking, credit risk, logistic regression, learning vector quantization

RePEc: http://ideas.repec.org/a/fau/fauart/v61y2011i4p384-402.html

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