Research on Fraud Detection Method of Financial Data of Listed Companies Based on HMCRAN

Abstract

This study investigates the application of deep learning technology to improve accuracy and efficiency in detecting financial fraud in listed companies. Using data from the China Economic and Financial Research Database on Main Board A-share listed companies with disclosed fraudulent behaviors, a hybrid multi-channel residual attention network (HMCRAN) was constructed, combining multi-channel residual networks and transformers. The study involved feature extraction, data preprocessing, model training, and performance evaluation, validating the superiority of the HMCRAN model for financial fraud detection. Experimental results demonstrate that the model excels in handling high-dimensional financial data and capturing long-term dependencies, significantly improving detection accuracy and robustness. The HMCRAN model outperforms traditional machine learning methods across various evaluation metrics, demonstrating its high practical application value.