Internet of Things Visualized Data Combined with Machine Learning for Intelligent Management and Analysis of Financial Data

This paper aims to explore the role of the Internet of Things (IoT) in intelligent financial data management and realize the visual management of financial data. This paper establishes an intelligent operation system for the expense reimbursement process and fund payment process for the intelligent management of enterprise financial data by studying the role of the IoT in the intelligent process of financial systems and how to visualize financial data. Then, different classification algorithms in Machine Learning (ML) are introduced to predict the financial crisis of listed enterprises with innovative science and technology in the Guotai Junan database, and feature screening and outlier processing are used to optimize them. Finally, based on the enterprise's existing financial data, it is predicted whether the enterprise will usher in a financial crisis in 2022 and become an unhealthy enterprise. Besides, the classification performance of five ML models is compared. The results show that the XGBoost model has the best classification performance before optimization, and the Area Under Curve (AUC) value is 0.9816. The final AUC value obtained by the Back Propagation (BP) neural network after the feature screening combination of the single-feature model is 0.9871, which is higher than the AUC value of the XGBoost model. It can be seen that the introduction of feature filtering can improve the forecast performance of the BP network for the financial crisis. Still, the index value of the outlier optimization BP model is reduced. From the side, the data quality in this paper is good, not affected by outliers, and it also reflects the reliability and practical significance of the dataset.