关键词: 连续比率模型, 有序分类数据, 消费金融, 信用风险管理

Abstract: Consumer finance has been developing rapidly in recent years in China, but it is also faced with risks of more complex fraud and credit. In order to better monitor the credit risk of consumers finance debtors, this paper proposes a new risk control method based on sparse structure continuation ratio model. Compared with traditional binary classification model, this model can handle ordinal response with three or more than three categories, which can estimate coefficients and meanwhile automatically conduct variable selection taking into account the structure information of coefficients in different sub-models. The Monte Carlo simulation results suggest that the proposed model has a good performance on variable selection and classification prediction.Finally, the proposed model is applied to the real consumer finance credit risk analysis, and we find that for debtors with insufficient credit information from traditional sources, by bringing high-frequency e-commerce behavior data and using the proposed high-dimensional ordinal multi-classification model, we can effectively identify the credit risks of the debtors and make up for the shortcoming of traditional credit method.

Key words: Continuation Ratio Model, Ordinal Categorical Data, Consumer Finance, Credit Risk Management