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Volume 16
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10.3390/info16100857
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Application of Generative AI in Financial Risk Prediction: Enhancing Model Accuracy and Interpretability

by
Kai-Chao Yao
1,2,3,
Hsiu-Chu Hung
1,
Ching-Hsin Wang
4,
Wei-Lun Huang
1,5,
Hui-Ting Liang
6,
Tzu-Hsin Chu
1,7,
Bo-Siang Chen
8,* and
Wei-Sho Ho
1,3,9,*
1
Department of Electrical and Mechanical Technology, National Changhua University of Education, Bao-Shan Campus, No. 2, Shi-Da Rd, Changhua 500208, Taiwan
2
Kenda Cultural and Educational Foundation, No. 146, Section 1, Zhongshan Rd., Yuanlin 510037, Taiwan
3
Graduate Institute of Technological and Vocational Education, National Changhua University of Education, Bao-Shan Campus, No. 2, Shi-Da Rd., Changhua 500208, Taiwan
4
Department of Healthcare Industry Technology Development and Management, National Chin-Yi University of Technology, Taichung 41170, Taiwan
5
Medical Affairs Office, National Taiwan University Hospital, No. 7, Zhongshan S. Rd., Zhongzheng Dist., Taipei 100225, Taiwan
6
Department of Finance, National Changhua University of Education, Bao-Shan Campus, No. 2, Shi-Da Rd., Changhua 500208, Taiwan
7
Department and Graduate Institute of Information Management, Yu Da University of Science and Technology, No. 168, Hsueh-fu Rd., Tanwen Village, Chaochiao Township, Miaoli 36143, Taiwan
8
Department of Vehicle Engineering, Nan Kai University of Technology, No. 568, Zhongzheng Rd., Caotun Township, Nantou 542020, Taiwan
9
NCUE Alumni Association, National Changhua University of Education Jin-De Campus, No. 1, Jinde Rd., Changhua 500207, Taiwan
*
Authors to whom correspondence should be addressed.
Information 2025, 16(10), 857; https://doi.org/10.3390/info16100857
Submission received: 9 August 2025 / Revised: 24 September 2025 / Accepted: 30 September 2025 / Published: 3 October 2025
(This article belongs to the Special Issue Modeling in the Era of Generative AI)
Versions Notes

Abstract

This study explores the application of generative artificial intelligence (AI) in financial risk forecasting, aiming to assess its potential in enhancing both the accuracy and interpretability of predictive models. Traditional methods often struggle with the complexity and nonlinearity of financial data, whereas generative AI—such as large language models and generative adversarial networks (GANs)—offers novel solutions to these challenges. The study begins with a comprehensive review of current research on generative AI in financial risk prediction, with a focus on its roles in data augmentation and feature extraction. It then investigates techniques such as Generative Adversarial Explanation (GAX) to evaluate their effectiveness in improving model interpretability. Case studies demonstrate the practical value of generative AI in real-world financial forecasting and quantify its contribution to predictive accuracy. Furthermore, the study identifies key challenges—including data quality, model training costs, and regulatory compliance—and proposes corresponding mitigation strategies. The findings suggest that generative AI can significantly improve the accuracy and interpretability of financial risk models, though its adoption must be carefully managed to address associated risks. This study offers insights and guidance for future research in applying generative AI to financial risk forecasting.
Keywords: generative AI; financial risk forecasting; model accuracy; model interpretability; data augmentation; generative adversarial networks generative AI; financial risk forecasting; model accuracy; model interpretability; data augmentation; generative adversarial networks

Share and Cite

MDPI and ACS Style

Yao, K.-C.; Hung, H.-C.; Wang, C.-H.; Huang, W.-L.; Liang, H.-T.; Chu, T.-H.; Chen, B.-S.; Ho, W.-S. Application of Generative AI in Financial Risk Prediction: Enhancing Model Accuracy and Interpretability. Information 2025, 16, 857. https://doi.org/10.3390/info16100857

AMA Style

Yao K-C, Hung H-C, Wang C-H, Huang W-L, Liang H-T, Chu T-H, Chen B-S, Ho W-S. Application of Generative AI in Financial Risk Prediction: Enhancing Model Accuracy and Interpretability. Information. 2025; 16(10):857. https://doi.org/10.3390/info16100857

Chicago/Turabian Style

Yao, Kai-Chao, Hsiu-Chu Hung, Ching-Hsin Wang, Wei-Lun Huang, Hui-Ting Liang, Tzu-Hsin Chu, Bo-Siang Chen, and Wei-Sho Ho. 2025. "Application of Generative AI in Financial Risk Prediction: Enhancing Model Accuracy and Interpretability" Information 16, no. 10: 857. https://doi.org/10.3390/info16100857

APA Style

Yao, K.-C., Hung, H.-C., Wang, C.-H., Huang, W.-L., Liang, H.-T., Chu, T.-H., Chen, B.-S., & Ho, W.-S. (2025). Application of Generative AI in Financial Risk Prediction: Enhancing Model Accuracy and Interpretability. Information, 16(10), 857. https://doi.org/10.3390/info16100857

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