This project focuses on analyzing the actual controller networks of listed companies and identifying financial fraud, with the aim of providing new theoretical perspectives and practical tools to enhance the quality of listed companies and promote the integrity of the capital market. We leverage big data and machine learning technologies to thoroughly analyze the relationship networks of actual controllers, encompassing multidimensional information such as controlling shareholders, shareholder networks, suppliers, and other third parties. The analysis also incorporates data on economic activities, media coverage, and legal records related to these networks. By constructing financial fraud prediction models and behavioral supervision models, this research seeks to develop innovative methods for detecting financial fraud. The findings will introduce fresh perspectives and tools while providing valuable references and guidance for capital market regulators, listed companies, investors, and auditing institutions.
