An analysis on Financial Statement Fraud Detection for Listed Companies using DCNN-LSTM-AE-AM Model

Financial fraud is a huge problem all over the world and has a negative impact on the growth of financial markets. It is challenging to identify frauds with a heavily skewed dataset since there are so many legitimate firms compared to fraudulent ones. Algorithms that can intelligently detect financial statement fraud have so been created to assist stakeholders in making decisions. Therefore, this research aims to improve financial statement fraud detection by integrating numerical data with cutting-edge deep learning algorithms. It is important to perform data preparation, feature extraction, and model training in this specific order. Fraud detection for narratives in annual reports is a part of data preprocessing that includes establishing a library of fraudulent feature terms and clustering yearly reports. Methods for Evaluating Vocabulary Similarity rely on feature extraction. Training DCNN-LSTMAE-AM models begins with feature retrieval. The suggested approach achieves better results than two state-of-the-art methods: LSTM and DCNN. After using the method, accuracy went up by $\mathbf{9 7. 4 8 \%}$.