Abstract: Default risk control is a key business component of credit loan services,which directly affects the profitability and bad-debt rate of lenders.With the development of the mobile Internet,credit-based financial services have benefited the general public.Default risk control has changed from manual judgment based on rules to credit models built by using large amounts of customer data to predict the default rate of customers.Relevant models include traditional machine learning models and deep learning mo-dels.The former has a strong interpretability but a weak predictability;the latter has a strong predictability but a poor interpre-tability,which is prone to overfitting the training data.Therefore,the integration of traditional machine learning models and deep learning models has always been an active research area in credit modeling.Inspired by the wide & deep learning models in re-commendation systems,a credit model first can utilize traditional machine learning to capture features of the structured data,while a deep learning can capture features of the unstructured data.Then,the model combines two parts of the learned features and uses an additional linear layer to transform the hidden features.Finally,the model outputs the predicted default rate.This model neutralizes the advantages of traditional machine learning models and deep learning models.Experimental results show thatthe proposed model has a stronger capability to predict the default probability of customers.

Key words: Deep learning, Default risk control, Machine learning, Wide & deep learning models