Logistic and SVM Credit Score Models Based on Lasso Variable Selection

Qingqing Li
2019 Journal of Applied Mathematics and Physics  
There are many factors influencing personal credit. We introduce Lasso technique to personal credit evaluation, and establish Lasso-logistic, Lasso-SVM and Group lasso-logistic models respectively. Variable selection and parameter estimation are also conducted simultaneously. Based on the personal credit data set from a certain lending platform, it can be concluded through experiments that compared with the full-variable Logistic model and the stepwise Logistic model, the variable selection
more » ... iable selection ability of Group lasso-logistic model was the strongest, followed by Lasso-logistic and Lasso-SVM respectively. All three models based on Lasso variable selection have better filtering capability than stepwise selection. In the meantime, the Group lasso-logistic model can eliminate or retain relevant virtual variables as a group to facilitate model interpretation. In terms of prediction accuracy, Lasso-SVM had the highest prediction accuracy for default users in the training set, while in the test set, Group lasso-logistic had the best classification accuracy for default users. Whether in the training set or in the test set, the Lasso-logistic model has the best classification accuracy for non-default users. The model based on Lasso variable selection can also better screen out the key factors influencing personal credit risk. Journal of Applied Mathematics and Physics card business is increasing day by day, and the credit risk that comes with it is not to be underestimated. Credit scoring model has been the core of credit risk management. In fact, the credit scoring model is a statistical model that analyzes a large number of customers' historical data, extracts key factors affecting credit risk, and then constructs a suitable model to evaluate the credit risk of new applicants or existing customers. Therefore, the construction of the personal credit scoring model can respond to credit risk in a timely and effective manner, which will play an important role in both banks and regulatory authorities.
doi:10.4236/jamp.2019.75076 fatcat:bq77xw2rmfetzmo765q4tl6n74