Analysis of Artificial Intelligence-Based E-Commerce Transaction Anomaly Monitoring Strategies and Their Implementation Effects

Abstract

The rapid development of e-commerce is accompanied by the problem of a large number of abnormal transactions, and the development of artificial intelligence models has brought the possibility of large-scale real-time monitoring of transaction anomalies. The study integrates the transaction anomaly monitoring method based on the Light GBM model and multi-instance learning model, and trains the constructed combined model based on Taobao data. The average accuracy, precision, recall, and F1 values of the combined model are 0.977, 0.971, 0.973, and 0.972, respectively. The monitoring effect is higher than that of a single model, which indicates that the combined model is more effective in identifying anomalies in e-commerce transactions. The relevant technicians of A e-commerce platform who have used the model said that the implementation effect of the combination model constructed in this paper is more satisfactory.

Keywords

• light gbm model
• multi-instance learning model
• combined model
• transaction anomaly monitoring
• 68t42