基于稀疏结构连续比率模型的消费金融风控研究

近年来,我国消费金融发展迅速,但同时也面临着更加复杂的欺诈和信用风险,为了更好地对消费金融中借贷客户的信用风险进行监测,本文提出了基于稀疏结构连续比率模型的风控方法。相对于传统的二分类模型,该模型的特点是可以处理借贷客户被分为三类或三类以上的有序数据,估计系数的同时能从众多纷繁复杂的数据中自动筛选重要变量,并在变量筛选过程中考虑不同子模型系数的结构特征。通过蒙特卡洛模拟发现,本文所提出的稀疏结构连续比率模型在分类泛化误差和变量筛选上的表现都较好。最后将本文提出的模型应用到实际的消费金融信用风险分析中,针对传统征信信息不足的借款人,通过引入高频电商消费行为数据,利用本文提出的高维有序多分类模型能有效识别借款人的信用风险,可以弥补传统征信方法的不足。

Sparse Structural Continuation Ratio Model with Its Application in Consumer Finance Risk Control

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.