Collection Scorecard Model Integrating Loan Information and Trajectory Features
This paper proposes a post-loan overdue risk of vehicle prediction model based on XGBoost-SMOTE to address the problems of the existing monitoring methods which can not identify risks effectively.In this paper,over sampling method of SMOTE is used to build a relatively balanced positive and negative proportion data set,which improves the generalization ability of the mod-el.And combined with the basic information of vehicle users,loan information and vehicle GPS track information for feature engi-neering.Then,the integrated feature data is input into XGBoost classifier,and finally complete the identification of post-loan over-due risk of vehicle.Compared with other machine learning algorithm prediction models,this model can achieve better prediction per-formance after the experiment.
国家科技期刊平台
NSTL
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