Abstract
The current method’s e‐commerce credit risk assessment is prone to poor data balance and low evaluation accuracy. An RB‐XGBoost algorithm‐based e‐commerce credit risk assessment model is proposed in this study. The adaptive random balance (RB) method is used to sample and process the obtained data to improve the balance degree of the data. An assessment index system is constructed based on the processed data. Based on the risk evaluation index system and the XGBoost algorithm, this paper constructed an e‐commerce risk assessment model and assessed the e‐commerce credit risk using this model. The experimental results show that the proposed method has good data balance, a high kappa coefficient, and a large receiver operating characteristic (ROC) curve area, which can effectively improve e‐commerce credit risk assessment accuracy.
Highlights
At present, e-commerce has entered society, and informatization has become an inevitable trend and core content of e-commerce, which has a significant impact on the fields of culture, society, and politics [1, 2]
The e-commerce credit risk assessment model based on the RB-XGBoost algorithm is used to sample and process e-commerce risk data through the adaptive random balance RB method to reduce the imbalance of data [8,9,10]
To verify the effectiveness of the RB-XGBoot algorithmbased e-commerce credit risk assessment model, it is necessary to carry out a test
Summary
E-commerce has entered society, and informatization has become an inevitable trend and core content of e-commerce, which has a significant impact on the fields of culture, society, and politics [1, 2]. Chang et al determines the risk assessment indicators based on the actual transaction situation and relevant literature and constructs a two-layer hybrid model to evaluate the credit risk of e-commerce combined with the back propagation (BP) neural network and naive Bayesian algorithm [7]. This method has relatively high assessment stability but does not process the data set before assessment, resulting in the Journal of Sensors unsatisfactory effect of the ROC curve obtained by this method and the problem of low assessment accuracy. An e-commerce credit risk assessment model based on the RBXGBoost algorithm is proposed to solve the issues in the above methods
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