Article: Financial risk prediction and warning system based on SGAN deep learning Journal: International Journal of Information and Communication Technology (IJICT) 2025 Vol.26 No.37 pp.1 - 17 Abstract: In response to the problem of insufficient performance of traditional financial risk warning models in class imbalanced data, this study proposes a deep learning warning method based on semi supervised generative adversarial network (SGAN). Firstly, construct a financial feature system and perform data balancing processing through SMOTE Tomek mixed sampling. Then, the model adopts a generator discriminator dual network architecture and constructs a composite loss function using cross entropy loss and Wasserstein distance. Finally, the experimental section selects financial data of Chinese A-share listed companies from 2016 to 2020, and uses transfer learning strategy to fine tune the pre trained model in the manufacturing industry to the retail industry. The empirical results show that this method improves the F1 score (0.87) and AUC value (0.92) compared to traditional logistic regression, effectively solving the early warning problem caused by the temporal correlation and industry heterogeneity of financial data. Inderscience Publishers - linking academia, business and industry through research

Title: Financial risk prediction and warning system based on SGAN deep learning

Authors: Linjing Dang

Addresses: Northwest University of Political Science and Law, Xi'an, 710122, China

Abstract: In response to the problem of insufficient performance of traditional financial risk warning models in class imbalanced data, this study proposes a deep learning warning method based on semi supervised generative adversarial network (SGAN). Firstly, construct a financial feature system and perform data balancing processing through SMOTE Tomek mixed sampling. Then, the model adopts a generator discriminator dual network architecture and constructs a composite loss function using cross entropy loss and Wasserstein distance. Finally, the experimental section selects financial data of Chinese A-share listed companies from 2016 to 2020, and uses transfer learning strategy to fine tune the pre trained model in the manufacturing industry to the retail industry. The empirical results show that this method improves the F1 score (0.87) and AUC value (0.92) compared to traditional logistic regression, effectively solving the early warning problem caused by the temporal correlation and industry heterogeneity of financial data.

Keywords: supervised generative adversarial network; SGAN; financial risk warning; SMOTE Tomek mixed sampling; generator discriminator dual network.

DOI: 10.1504/IJICT.2025.149182

International Journal of Information and Communication Technology, 2025 Vol.26 No.37, pp.1 - 17

Received: 24 May 2025
Accepted: 13 Jun 2025
Published online: 16 Oct 2025 *

Title: Financial risk prediction and warning system based on SGAN deep learning

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