Abstract
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 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.
Highlights
In the 21st century, with the rapid development of China’s economy, the concept of Chinese people’s consumption has undergone tremendous changes, and the credit industry has developed rapidly
We introduce Lasso technique to personal credit evaluation, and establish Lasso-logistic, Lasso-support vector Machine (SVM) and Group lasso-logistic models respectively
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 ability of Group lasso-logistic model was the strongest, followed by Lasso-logistic and Lasso-SVM respectively
Summary
In the 21st century, with the rapid development of China’s economy, the concept of Chinese people’s consumption has undergone tremendous changes, and the credit industry has developed rapidly. 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. In this era of information explosion, the emergence of big data has led to some credit information, and the existing scoring models often cannot effectively screen out dangerous customers. The calculation of subset selection is quite complicated In view of these defects, we adopt the Lasso method which can simultaneously perform variable selection and parameter estimation. The prediction accuracy of several models for default users is compared
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