Application of artificial intelligence Sensors based on random forest algorithm in financial recognition models

Abstract

Due to the important role of financial information disclosed by enterprises in investor decision-making and economic policy formulation, the corporate market has set the latest standards and requirements for the quality of financial reports. In the current situation, financial recognition work is mainly completed manually, which is inefficient and prone to errors, and is not suitable for large-scale applications. Therefore, we conduct in-depth research on financial recognition models. Firstly, we analyzed the random forest algorithm and used the SMOTE algorithm to expand the sample data. We successfully constructed an optimized random forest algorithm model, which can better improve accuracy and reduce misjudgment rates. Secondly, combined with the current financial data analysis of artificial intelligence technology, the steps for data management and data extraction were designed. Finally, while establishing a financial recognition model, a channel for obtaining internet information and a program for processing this information were also established. After experimental verification, the random forest model has shown better fitting performance in the field of financial fraud identification, providing more reliable support for applications in this field. Therefore, the financial recognition model constructed using artificial intelligence technology based on random forest algorithm has significant value in both theoretical and practical aspects, effectively reducing corporate financial risks.

Keywords

• Random forest algorithm
• Artificial intelligence
• Financial identification model
• Data processing