7The Extraction of Features That Characterize Financial Fraud Behavior by Machine Learning Algorithms
George X. Yuan1,2,3, Yuanlei Luo4‡, Lan Di5†, Yunpeng Zhou3, Wen Chen3, Yiming Liu3 and Yudi Gu6*
1College of Science, Chongqing University of Technology, Chongqing, China
2Business School, Chengdu University, Chengdu, China
3Shanghai Hammer Digital Tech. Co. Ltd. (Hammer), Shanghai, China
4Research Center, China Institute of Ocean Engineering, Beijing, China
5School of AI and Computer Science, Jiangnan Univ., Wuxi, China
6Center of Information Construct and Management, Jiangnan University, Wuxi, China
Abstract
The purpose of this paper is to discuss how to use machine learning algorithms to screen the features and related applications of the characteristic indicators in describing a company’s financial fraud behavior under the framework of big data analysis. In particular, we first screen the “characteristic indicator” (features) related to the financial anomalies due to a company’s fraudulent financial reports then extract (fraud) features based on the corresponding fraudulent actions. In addition, the validation test is carried out to test the ability of fraud features’ (indicators’) performance in identifying and providing warning signals on fraud events. Specifically, by extracting the characteristics (features) of fraudulent financial behaviors related to financial frauds, based on traditional (structure) financial data and unstructured corporate governance structures, ...
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