Financial Fraud Recognition Method for Listed Companies Based on Deep Learning and Textual Emotion

Financial fraud of listed companies refers to the untrustworthy behavior of distorting accounting information by improper means, which has a negative impact on company operations, economic development, and social interests. At present, more research focuses on financial digital data, and less research on text information and deep learning algorithms. Therefore, a financial fraud recognition method for listed companies based on deep learning and textual emotional feature is proposed. Firstly, the method selects and preprocesses the financial indicators, and uses Bi-LSTM to extract the emotional features of the stock review text. Then, the method uses the RCC (residual-cross-convolutional) parallel network to recognize financial fraud. The network uses residual network, cross network, convolutional network and long short-term memory network to extract financial fraud features in parallel, and uses batch normalization and full connection to obtain the final recognition result. The experiment results show that this method achieves better results than other models in recognizing financial fraud for listed companies, with a recall rate and AUC of 88.46% and 82.06% respectively. © 2024 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.