Abstract: Credit evaluation model plays an important role in helping financial institutions to identify default risk. However, due to the imbalance of the proportion of default and non-default samples, there are the phenomena of over-recognition for non-default samples and under-recognition for default samples. Some of default samples, named hard samples, are difficult to be identified. Therefore, the key to improve the prediction performance of the model is to improve its ability to recognize the hard samples. In practice, the existing deep learning credit evaluation model, which takes the Cross Entropy as the loss function, considers that there is no difference between the contribution of the hard samples and the simple samples to the target loss. It affects the effective identification of hard samples by the model.

Key words: credit evaluation; focal loss; BP neural network; adaptive comprehensive oversampling