Risk Data Analysis of Cross Border E-commerce Transactions Based on Data Mining

Abstract

Abstract At present, China’s foreign trade volume of cross-border e-commerce is still relatively small, the targeted legal system has not been established, and the industry access threshold is relatively low. The development of cross-border e-commerce faces many risks. Relying on data mining technology, data docking and risk control. At the data level, the risk of cross-border e-commerce transaction is processed and analyzed, and the potential risk is predicted, and more targeted risk control methods are proposed to reduce the risk of cross-border e-commerce transaction business. Aiming at this problem, this paper establishes the risk data analysis of cross-border e-commerce transactions based on data mining. In order to further verify the data accuracy of data mining technology, this paper analyzes the test values of neural network algorithm, seizure rate and inspection rate. When the misclassification loss parameter value is increased from 1 to 4, the detection rate of the model increases and the detection rate decreases. When the misclassification loss value is higher than 4, the detection rate increases, but the seizure rate decreases significantly. Therefore, the target inspection rate of the model can be changed by adjusting the misclassification loss parameter value of the model. If we set the parameter value of misclassification loss reasonably, we can achieve the goal of preventing and controlling the maximum risk with the lowest data mining cost. Through the analysis, the research in this paper has achieved ideal results, and made a contribution to data mining in cross-border e-commerce transaction risk data analysis.

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