Research on Matrix-Based Algorithm for Binary Tree Model Option Pricing

With the rapid development of internet finance, financial risk issues have become increasingly prominent, and traditional risk control models can no longer effectively address the complex and dynamic scenarios of internet finance. This paper leverages the advantages of machine learning algorithms to propose an internet financial risk control model based on machine learning. Firstly, the data characteristics of financial risk control scenarios are analyzed, and data preprocessing and feature extraction are performed to improve the quality of model input. Secondly, to meet different risk identification requirements, various machine learning algorithms, including decision trees, random forests, support vector machines, and deep learning models, are selected to construct and optimize the risk control model. Experimental verification and comparative analysis are conducted to evaluate the performance of each algorithm in risk control. The results demonstrate that the machine learning-based risk control model significantly outperforms traditional methods in terms of precision and recall for risk identification. Furthermore, real-world case studies validate the model's effectiveness, proving its practicality and reliability in the field of internet financial risk control. Finally, the paper summarizes the main conclusions of the research and proposes directions for further model optimization and scenario expansion, providing technical support and theoretical reference for internet financial risk control.