Exploration on Financial Risk Supervision System Based on Internet of Things and Improved Ant Colony Optimization Algorithms

With the development of financial markets and the acceleration of globalization, financial risks have become more complex and diverse, and the traditional risk management methods are inefficient and inaccurate.In order to ensure the stable operation of financial institutions and protect the interests of investors, a financial risk supervision system based on the Internet of things and improved ant colony algorithm is designed to monitor the risk status of financial institutions in real time.The Internet of Things technology is used to collect and transmit financial regulatory data, and the collected data is used to assess and monitor the risks of financial markets and transactions, so as to provide decision support for regulators.The improved ant colony algorithm is used to optimize and improve the financial risk assessment model to improve the accuracy and efficiency of the assessment.The effectiveness and performance of the regulatory system in risk regulation are analyzed by testing and testing the system through experiments.In order to verify the effectiveness of the regulatory system, financial market and transaction data collected using iot technology are compared with traditional financial risk regulation methods.After a series of experiments, the average risk assessment accuracy of the system is 88.90%, the average financial risk supervision efficiency is 96.32%, and the average scalability score is 94.45.The system designed in this paper has good performance and can well meet the needs of financial risk supervision under the current complex economic situation.