Financial crisis monitoring and prevention of listed companies based on the internet of things and deep learning

This study develops a financial crisis prediction and monitoring system for listed companies based on IoT and deep learning (DL) to make up for the lack of real-time financial risk management. Based on a multi-layer IoT framework (perception layer, network layer, and application layer) for data collection, a hybrid deep learning model integrating convolutional neural network (CNN) and autoencoder is designed. The model processes 20 financial indicators covering the dimensions of solvency, profitability, operational efficiency, and crisis level. Validated on the K company dataset, the architecture reduces the features from 20 to 5 through a hierarchical structure, and the prediction accuracy reaches 90% (2022), 94% (2023), and 96% (2024), respectively. The results show that the accuracy rate remains above 90% within three years, confirming that the IoT-DL fusion can effectively extract potential risk patterns and achieve early crisis detection. The system can provide proactive financial protection, prompting enterprises to adopt real-time monitoring, diversified financing, and optimize cash flow. Its practical significance lies in the ability to build a scalable risk warning framework for data-driven decision-making in turbulent markets.