期刊
COMPLEX & INTELLIGENT SYSTEMS
卷 9, 期 2, 页码 1391-1414出版社
SPRINGER HEIDELBERG
DOI: 10.1007/s40747-022-00854-y
关键词
Credit risk; Auto loans; Borderline-Smote; Filter-Wrapper feature selection; PSO-XGBoost
This paper investigates the credit risk assessment mechanism for personal auto loans. A machine learning based model incorporating Smote-Tomek Link algorithm and Filter-Wrapper feature selection method is proposed. By combining Particle Swarm Optimization and eXtreme Gradient Boosting model, a superior PSO-XGBoost model is formed, showing better classification performance and effect.
As online P2P loans in automotive financing grows, there is a need to manage and control the credit risk of the personal auto loans. In this paper, the personal auto loans data sets on the Kaggle platform are used on a machine learning based credit risk assessment mechanism for personal auto loans. An integrated Smote-Tomek Link algorithm is proposed to convert the data set into a balanced data set. Then, an improved Filter-Wrapper feature selection method is presented to select credit risk assessment indexes for the loans. Combining Particle Swarm Optimization (PSO) with the eXtreme Gradient Boosting (XGBoost) model, a PSO-XGBoost model is formed to assess the credit risk of the loans. The PSO-XGBoost model is compared against the XGBoost, Random Forest, and Logistic Regression models on the standard performance evaluation indexes of accuracy, precision, ROC curve, and AUC value. The PSO-XGBoost model is found to be superior on classification performance and classification effect.
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