Artificial intelligence accelerates the development of the risk control industry. Undoubtedly, risk control is the core of intelligent risk control, and a credit default prediction model is its essential means. The traditional access to risk control is based on artificial and generalized linear models. However, the data of transactions completed on the Internet are characterized by high dimensions and multiple sources, which cannot be processed by existing models. This poses a great challenge to traditional risk control. In view of this, this study proposes an interpretable credit default model based on the fusion method. To be specific, the accuracy of the prediction results is first enhanced through the fusion of base models (LightGBM, DeepFM, and CatBoost) and secondary model (CatBoost). Then, the prediction result of the fusion model is interpreted by the introduced local-based interpretability method LIME that is independent of the model. According to the experimental result of a real dataset, the satisfactory accuracy and interpretability of the model can be witnessed on the task of credit default prediction.