Innovation in Financial Enterprise Risk Prediction Model
Year: 2024
Type: article
Abstract: In the context of predicting financial risks for enterprises, traditional methods are inadequate in capturing complex multidimensional data features, resulting in suboptimal prediction performance. Although existing deep learning techniques have shown some improvements, they still face challenges in processing time series data and detecting extended dependencies. To address these issues, this paper proposes an integrated deep learning framework utilizing Convolutional Neural Network (CNN), Transformer model, and Wavelet Transform (WT). The proposed model leverages CNN to derive local features from the data, employs the Transformer to capture long-term dependencies, and uses WT for multiscale analysis, thereby enhancing the accuracy and stability of predictions. Experimental results demonstrate that the CNN-Transformer-WT model performs excellently across various datasets, including Kaggle Dataset (Credit Card Fraud Detection Dataset), Bank Marketing Dataset, and Yahoo Finance Historical Stock Market Dataset.
DOI: 10.4018/joeuc.361650
Authors: Jing Jin, Yongqing Zhang
Institution: University of Shanghai for Science and Technology
Language: English
Subfield: Accounting
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W4404843584