Personal credit risk identification based on combined machine learning model

Personal credit risk assessment is essentially based on the classification of indicators or characteristic variables related to personal credit. Currently, a single model is often used to classify normal customers and defaulting customers. However, the single model may face the risk of a higher type II error rate, that is, defaulting customers are misjudged as normal customers. From the perspective of the combined model, this paper uses the confidence-weighted voting strategy to give a single model different weights to construct a C5.0-SVM combined model. The study finds that the total classification accuracy of the C5.0-SVM combined model is higher than that of the single model, and the type II error rate is lower than that of the single model.