Design and Implementation of Cross-border E-commerce Risk Control System Based on Machine Learning Algorithm and Computer Simulation

With the rapid growth of cross-border e-commerce, transaction risks have increasingly become a major barrier to its sustainable development. This paper presents a design and implementation proposal for a risk control system that leverages machine learning algorithms and computer simulation. The system utilizes machine learning techniques to analyze and mine large volumes of transaction data, identifying potential risk factors and constructing risk assessment models. Specifically, the system first performs data preprocessing and feature selection to extract critical features that impact transaction security. Subsequently, various machine learning algorithms (such as decision trees, random forests, and support vector machines) are employed for model training and optimization to enhance the accuracy of risk prediction. In terms of implementation, the system uses computer simulation technology to conduct simulated tests of risk control strategies, evaluating their effectiveness and stability across different scenarios. Through simulation results, the system can dynamically adjust risk control strategies, achieving real-time monitoring and early warning. Ultimately, this research provides a scientific and effective risk control solution for cross-border e-commerce enterprises, enhancing transaction security and supporting sustainable business growth. Validation through practical cases demonstrates that the proposed risk control system shows promising applications in reducing transaction risks and improving user satisfaction.