  Credit risk modelling is an integral part of the global financial system.
While there has been great attention paid to neural network models for credit
default prediction, such models often lack the required interpretation
mechanisms and measures of the uncertainty around their predictions. This work
develops and compares Bayesian Neural Networks(BNNs) for credit card default
modelling. This includes a BNNs trained by Gaussian approximation and the first
implementation of BNNs trained by Hybrid Monte Carlo(HMC) in credit risk
modelling. The results on the Taiwan Credit Dataset show that BNNs with
Automatic Relevance Determination(ARD) outperform normal BNNs without ARD. The
results also show that BNNs trained by Gaussian approximation display similar
predictive performance to those trained by the HMC. The results further show
that BNN with ARD can be used to draw inferences about the relative importance
of different features thus critically aiding decision makers in explaining
model output to consumers. The robustness of this result is reinforced by high
levels of congruence between the features identified as important using the two
different approaches for training BNNs.
