关键词: 多源数据, 整合治愈率模型, 违约日期预测, 信用评分

Abstract: Traditional credit scoring method, based on statistical classification, can only predict whether an applicant will default in the future, but cannot predict when he is likely to default. The cure rate model,which incorporates two submodels—binary classification and survival model, can predict not only whether a default will occur but also when it will occur. Furthermore, with the development of big data, more and more data sources have emerged. One can collect multiple data sets for the same or similar tasks. Motivated by this,an integrative cure rate model with multi-source data has been proposed in this paper, which can simultaneously model on multiple datasets and estimate parameters. Composite penalty function is adopted to select important groups as well as important members of those groups. Similarity in signs of two submodels’ coefficients is promoted to improve the interpretability of the model. Numerical simulation shows the obvious advantages of our proposal in both variable selection and parameter estimation. Finally, the proposed method is applied to the default point prediction and performs well.

Key words: Multi-source Data, Integrative Cure Rate Model, Default Point Prediction, Credit Scoring