Does Non-linearity Matter in Retail Credit Risk Modelling?

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 bench-marking (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.