  In this article we compare the performances of a logistic regression and a
feed forward neural network for credit scoring purposes. Our results show that
the logistic regression gives quite good results on the dataset and the neural
network can improve a little the performance. We also consider different sets
of features in order to assess their importance in terms of prediction
accuracy. We found that temporal features (i.e. repeated measures over time)
can be an important source of information resulting in an increase in the
overall model accuracy. Finally, we introduce a new technique for the
calibration of predicted probabilities based on Stein's unbiased risk estimate
(SURE). This calibration technique can be applied to very general calibration
functions. In particular, we detail this method for the sigmoid function as
well as for the Kumaraswamy function, which includes the identity as a
particular case. We show that stacking the SURE calibration technique with the
classical Platt method can improve the calibration of predicted probabilities.
