%0 Journal Article
%T 企业财务欺诈异常检测及其最新进展<br>Corporate Financial Fraud Anomaly Detection and Its Latest Progress
%A 付秀莲
%A 肖惠予
%J Modern Management
%P 566-573
%@ 2160-732X
%D 2024
%I Hans Publishing
%R 10.12677/mm.2024.144069
%X 财务欺诈对经济市场的稳定性和信任体系造成了重大威胁，准确识别和检测财务欺诈异常变得至关重要。本文综述了企业财务欺诈异常检测的最新进展。首先介绍了传统的财务欺诈检测方法，重点讨论了基于机器学习和数据挖掘技术的财务欺诈检测方法。这些方法利用丰富的财务数据和模式识别算法，可以自动发现潜在的欺诈行为。接着介绍了出现的新方法：将财务报表、媒体新闻、企业公告等文本数据纳入检测范围。新方法通过提取更多的特征信息、融合多种数据源的方式，提高了财务欺诈检测的准确性和效率。<br />
Financial fraud poses a major threat to the stability and trust system of the economic market, so it is very important to accurately identify and detect financial fraud anomalies. This paper reviews the latest progress in anomaly detection of financial fraud in enterprises. Firstly, the traditional financial fraud detection methods are introduced, and the financial fraud detection methods based on machine learning and data mining technology are mainly discussed. These methods use rich financial data and pattern recognition algorithms to automatically spot potential fraud. Then, a new method is introduced: text data such as financial statements, media news and corporate announcements are included in the scope of detection. The new method improves the accuracy and efficiency of financial fraud detection by extracting more characteristic information and integrating multiple data sources.
%K 财务欺诈，异常检测，机器学习，数据挖掘，文本数据<br>Financial Fraud
%K Anomaly Detection
%K Machine Learning
%K Data Mining
%K Text Data
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=84826