Research on Development of Financial Fraud Application Tool Based on Adaptive Entropy Projection Clustering Algorithm for Financial Report Text Data Mining

Abstract

The problem of financial fraud in listed companies in China has occurred from time to time, seriously jeopardizing the interests of the financial reporting information demand side and disrupting the normal operation of the capital market. The effective supervision of listed companies' financial reports is an effective means to achieve financial fraud control. However, due to the huge amount of corporate financial report data, it is urgent to develop financial fraud application tools suitable for financial report text data mining. Therefore, this paper constructs an adaptive entropy projection clustering model, and uses the adaptive entropy projection clustering algorithm to develop and design financial reporting text data mining and forecasting financial fraud application tools, in order to identify financial fraud enterprises to a large extent. Users of financial information can make better use of the financial information disclosed by listed companies, and make correct decisions, and promote the sound development of the capital market.