Applications of Generative AI in Accounting
DOI:
https://doi.org/10.71204/2hckfj51Keywords:
Generative AI, Intelligent Transformation, Human-AI Collaboration, Risk and RegulationAbstract
With the rapid advancement of generative AI technology, its powerful capabilities in content generation and code writing are transforming industries worldwide. The traditional accounting industry stands at a critical juncture of intelligent transformation, facing urgent demands to enhance efficiency and reduce costs. This paper systematically explores the impact of generative AI on accounting workflows, analyzes specific application scenarios, and examines accompanying issues such as data security and responsibility delineation. Ultimately, it proposes a coexistence model between human and AI workforces, addressing implementation strategies, talent development, and risk oversight.
References
Brown, A., & Davis, K. (2022). Generative AI in Financial Reporting: Opportunities and Challenges. Journal of Accounting Technology, 15(3), 45-60.
Brown, T. B., Mann, B., Ryder, N., et al. (2020). Language models are few-shot learners. Advances in neural information processing systems, 33, 1877-1901.
Chen, X., Wang, L., & Li, S. (2022). Integrating External Textual Data for Cash Flow Forecasting with Generative AI. Journal of Forecasting, 41(2), 210-225.
Davenport, T. H., & Ronanki, R. (2018). Artificial Intelligence for the Real World. Harvard Business Review, 96(1), 108-116.
Deloitte. (2024). The Future of Finance: Unleashing the Power of Generative AI. Deloitte Insights.
Goodfellow, I., Pouget-Abadie, J., Mirza, M., et al. (2014). Generative adversarial nets. Advances in neural information processing systems, 27, 1-9.
Ho, J., Jain, A., & Abbeel, P. (2020). Denoising diffusion probabilistic models. Advances in neural information processing systems, 33, 6840-6851.
Jiang, L., Zhang, H., & Wang, Y. (2021). Automating Accounting Entries with Natural Language Processing. Proceedings of the International Conference on Artificial Intelligence in Finance, 112-125.
KPMG. (2023). Generative AI in Audit: Transforming Quality and Insight. KPMG International.
Liu, X., Chen, Z., & Yang, R. (2023). Case Study: AI-Driven Accounting Transformation in Chinese SMEs. Accounting and Finance Review, 28(2), 88-102.
Moffitt, K. C., Vasarhelyi, M. A., & Warren, J. D. (2020). Robotic Process Automation for Accounting: A Review and Research Agenda. Journal of Information Systems, 34(1), 1-21.
Richins, G., Stapleton, A., & Stratopoulos, T. C. (2017). Big Data Analytics: Opportunity or Threat for the Accounting Profession? Journal of Information Systems, 31(3), 63-79.
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., & Ommer, B. (2022). High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 10684-10695).
Smith, T., & Johnson, P. (2020). AI-Powered Reconciliation in Modern Accounting Systems. Journal of Financial Innovation, 12(4), 33-47.
Sutton, S. G. (2006). The Role of AIS Research in Guiding Practice. International Journal of Accounting Information Systems, 7(1), 1-4.
Vaswani, A., Shazeer, N., Parmar, N., et al. (2017). Attention is all you need. Advances in neural information processing systems, 30.
Wilson, R., & Lee, M. (2019). Risk Detection in Financial Contracts Using Generative AI. Harvard Business Review, 97(5), 78-91.
Bommasani, R., Hudson, D. A., et al. (2021). On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258.
Zhang, C., Dai, J., & Xu, Y. (2022). The Impact of Artificial Intelligence on Corporate Accounting and Auditing: A Literature Review. Technological Forecasting and Social Change, 174, 121242.
Zhang, Z., Han, X., Zhou, H., et al. (2022). CPM-2: Large-scale cost-effective pre-trained language models. AI Open, 3, 216-225.
Zhao, W. X., Zhou, K., Li, J., et al. (2023). A survey of large language models. arXiv preprint arXiv:2303.18223.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Yuxuan Zhang, Hanwen Zhao (Author)

This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles published in this journal are licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author(s) and source are properly credited. Authors retain copyright of their work, and readers are free to copy, share, adapt, and build upon the material for any purpose, including commercial use, as long as appropriate attribution is given.
