Application of Random Forest Algorithm in Accounting Data Analysis and Prediction

Accounting data is an important source of information reflecting a company's financial status, operating conditions, and cash flow. This study selected three typical machine learning models: random forest, decision tree, and support vector machine, and applied them to two tasks, namely, anomaly detection of accounting data and measurement of data analysis time. In order to analyze the superiority of the random forest algorithm, this paper compares the performance of these three models on the above indicators. At the same time, the study also attempts to explore the possible impact of factors such as data characteristics, model structure, and computational complexity on the results. Compared with other algorithms (decision tree and support vector machine) in the task of anomaly detection of accounting data, it performs well, with an accuracy of 0.94 to 0.98. In addition, in terms of data analysis time, the speed of the random forest algorithm is only slightly slower than that of the decision tree, but significantly faster than the support vector machine. These findings show that the random forest algorithm not only has high detection accuracy when processing accounting data, but can also complete the analysis within a reasonable time, providing an effective tool for financial monitoring and decision support for enterprises.