Internet financial institutions have many credit businesses, and some of the newly launched businesses cannot establish an effective credit scoring model due to the lack of customer data. This work studies the application of transfer learning ideas to this problem and uses existing customer data from other businesses to help new businesses build effective credit scoring models. This study proposes a deep learning method based on the combination of Triplet-Loss and domain adaptation to re-encode existing business data, and transfers the knowledge obtained after re-encoding to the model of the new business, and finally uses XGBoost as the classifier. In view of the above problems, the model proposed in this study has improved the effect compared to traditional machine learning methods, and solved the problem to a certain extent.