Large Language Models in Accounting and Financial Research: A Review of Applications in Accounting-related Text Analysis

Research Article
Open access

Large Language Models in Accounting and Financial Research: A Review of Applications in Accounting-related Text Analysis

Yan Hao 1*
  • 1 School of Management, Hefei University of Technology, Hefei, China    
  • *corresponding author 1321498664@qq.com
AEMPS Vol.189
ISSN (Print): 2754-1177
ISSN (Online): 2754-1169
ISBN (Print): 978-1-80590-179-2
ISBN (Online): 978-1-80590-180-8

Abstract

With the rapid advancement of large language models (LLM) technologies, new technical support and research paradigms have emerged for traditional accounting and finance studies. Despite these opportunities, scholars in the fields of accounting and finance often encounter challenges when navigating the extensive and complex domain knowledge of large language models, as well as the ever-evolving academic literature. To address this gap, this paper conducts a qualitative survey on the applications of large language models in early accounting and finance researches. This paper is structured into three main sections. First, it delves into the definition, underlying principles, and developmental trajectory of large language models. Second, it synthesizes the latest research on large language models applications in accounting and finance, categorizing these studies into three major domains, including sentiment analysis, report analysis and practical works. Finally, the paper highlights emerging trends and potential research directions, aiming to provide a comprehensive guide for future scholarly exploration in this dynamic field. Although it still has many shortcomings such as high cost and black box of process, the application of large language modeling provides a new technical support for text analysis in accounting and financial research, and also improves the actual efficiency of accounting and auditing.

Keywords:

Large Language Model, Accounting Research, Financial Research, Text analysis

Hao,Y. (2025). Large Language Models in Accounting and Financial Research: A Review of Applications in Accounting-related Text Analysis. Advances in Economics, Management and Political Sciences,189,55-60.
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References

[1]. Chang Y, Wang, X, Wang, J, et al. A survey on evaluation of large language models[J], 2024, 15(3): 1-45.

[2]. Feuerriegel S, Hartmann, J, Janiesch, C, et al. Generative ai[J], 2024, 66(1): 111-126.

[3]. Naveed H, Khan, A U, Qiu, S, et al. A comprehensive overview of large language models[J]. arXiv preprint 2023: arXiv:.06435.

[4]. Teubner T, Flath, C M, Weinhardt, C, et al. Welcome to the era of chatgpt et al. the prospects of large language models[J], 2023, 65(2): 95-101.

[5]. Rosenfeld R. Two decades of statistical language modeling: Where do we go from here?[J]. Proceedings of the IEEE, 2000, 88(8): 1270-1278.

[6]. Horowitz J L, Savin, N J J o e p. Binary response models: Logits, probits and semiparametrics[J], 2001, 15(4): 43-56.

[7]. Vaswani A, Shazeer, N, Parmar, N, et al. Attention is all you need[J]. Advances in neural information processing systems, 2017, 30.

[8]. Devlin J, Chang, M-W, Lee, K, et al. Bert: Pre-training of deep bidirectional transformers for language understanding[A],Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, volume 1 (long and short papers)[C], 2019.

[9]. Kaplan J, McCandlish, S, Henighan, T, et al. Scaling laws for neural language models[J]. arXiv preprint, 2020: arXiv:.08361.

[10]. Wen Y, Liang, Y, Zhu, X. Sentiment analysis of hotel online reviews using the BERT model and ERNIE model-Data from China[J]. PLoS One, 2023, 18(3): e0275382.

[11]. Fatouros G, Soldatos, J, Kouroumali, K, et al. Transforming sentiment analysis in the financial domain with ChatGPT[J]. Machine Learning with Applications, 2023, 14.

[12]. Zhuang Y, Wang, F, Chiu, D K W, et al. Leveraging large language models to examine the interaction between investor sentiment and stock performance[J]. Engineering Applications of Artificial Intelligence, 2025, 150.

[13]. Zhen K, Xie, D, Hu, X. A multi-feature selection fused with investor sentiment for stock price prediction[J]. Expert Systems with Applications, 2025, 278.

[14]. Wang Q, Yiu Keung Lau, R, Xie, H, et al. Social Executives’ emotions and firm value: An empirical study enhanced by cognitive analytics[J]. Journal of Business Research, 2024, 175.

[15]. Zhu J, Zhang, C, Sun, J, et al. The impact mechanism of interactive carbon disclosure on firm value moderated by investors’ online social networks[J]. Research in International Business and Finance, 2025, 75: 102771.

[16]. Bhattacharya I, Mickovic, A. Accounting fraud detection using contextual language learning[J]. International Journal of Accounting Information Systems, 2024, 53.

[17]. Fan S, Wu, Y, Yang, R. Measuring firm-level supply chain risk using a generative large language model[J]. Finance Research Letters, 2025, 77.

[18]. Zou Y, Shi, M, Chen, Z, et al. ESGReveal: An LLM-based approach for extracting structured data from ESG reports[J]. Journal of Cleaner Production, 2025, 489.

[19]. Zhao J, Wang, X J J o C A, Finance. Unleashing efficiency and insights: Exploring the potential applications and challenges of ChatGPT in accounting[J], 2024, 35(1): 269-276.

[20]. Street D, Wilck, J, Chism, Z. Six principles for the effective use of ChatGPT and other large language models in accounting[J]. CPA Journal, 2023.

[21]. Fotoh L, Mugwira, T. Exploring Large Language Models (ChatGPT) in External Audits: Implications and Ethical Considerations[J]. Available at SSRN 4453835, 2023.

[22]. Street D, Wilck, J. 'Let’s Have a Chat': Principles for the Effective Application of ChatGPT and Large Language Models in the Practice of Forensic Accounting[J]. Journal of Forensic Investigative Accounting, 2023.

[23]. Boritz J E, Stratopoulos, T C. AI and the accounting profession: Views from industry and academia[J]. Journal of Information Systems, 2023, 37(3): 1-9.

[24]. Dong M M, Stratopoulos, T C, Wang, V X. A scoping review of ChatGPT research in accounting and finance[J]. International Journal of Accounting Information Systems, 2024, 55: 100715.

[25]. Yi Z, Cao, X, Chen, Z, et al. Artificial intelligence in accounting and finance: Challenges and opportunities[J]. IEEE Access, 2023, 11: 129100-129123.

Cite this article

Hao,Y. (2025). Large Language Models in Accounting and Financial Research: A Review of Applications in Accounting-related Text Analysis. Advances in Economics, Management and Political Sciences,189,55-60.

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The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of ICMRED 2025 Symposium: Effective Communication as a Powerful Management Tool

ISBN：978-1-80590-179-2(Print) / 978-1-80590-180-8(Online)
Editor：Lukáš Vartiak
Conference website: https://2025.icmred.org/Bratislava.html
Conference date: 30 May 2025
Series: Advances in Economics, Management and Political Sciences
Volume number: Vol.189
ISSN：2754-1169(Print) / 2754-1177(Online)

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References

[1]. Chang Y, Wang, X, Wang, J, et al. A survey on evaluation of large language models[J], 2024, 15(3): 1-45.

[2]. Feuerriegel S, Hartmann, J, Janiesch, C, et al. Generative ai[J], 2024, 66(1): 111-126.

[3]. Naveed H, Khan, A U, Qiu, S, et al. A comprehensive overview of large language models[J]. arXiv preprint 2023: arXiv:.06435.

[4]. Teubner T, Flath, C M, Weinhardt, C, et al. Welcome to the era of chatgpt et al. the prospects of large language models[J], 2023, 65(2): 95-101.

[5]. Rosenfeld R. Two decades of statistical language modeling: Where do we go from here?[J]. Proceedings of the IEEE, 2000, 88(8): 1270-1278.

[6]. Horowitz J L, Savin, N J J o e p. Binary response models: Logits, probits and semiparametrics[J], 2001, 15(4): 43-56.

[7]. Vaswani A, Shazeer, N, Parmar, N, et al. Attention is all you need[J]. Advances in neural information processing systems, 2017, 30.

[8]. Devlin J, Chang, M-W, Lee, K, et al. Bert: Pre-training of deep bidirectional transformers for language understanding[A],Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, volume 1 (long and short papers)[C], 2019.

[9]. Kaplan J, McCandlish, S, Henighan, T, et al. Scaling laws for neural language models[J]. arXiv preprint, 2020: arXiv:.08361.

[10]. Wen Y, Liang, Y, Zhu, X. Sentiment analysis of hotel online reviews using the BERT model and ERNIE model-Data from China[J]. PLoS One, 2023, 18(3): e0275382.

[11]. Fatouros G, Soldatos, J, Kouroumali, K, et al. Transforming sentiment analysis in the financial domain with ChatGPT[J]. Machine Learning with Applications, 2023, 14.

[12]. Zhuang Y, Wang, F, Chiu, D K W, et al. Leveraging large language models to examine the interaction between investor sentiment and stock performance[J]. Engineering Applications of Artificial Intelligence, 2025, 150.

[13]. Zhen K, Xie, D, Hu, X. A multi-feature selection fused with investor sentiment for stock price prediction[J]. Expert Systems with Applications, 2025, 278.

[14]. Wang Q, Yiu Keung Lau, R, Xie, H, et al. Social Executives’ emotions and firm value: An empirical study enhanced by cognitive analytics[J]. Journal of Business Research, 2024, 175.

[15]. Zhu J, Zhang, C, Sun, J, et al. The impact mechanism of interactive carbon disclosure on firm value moderated by investors’ online social networks[J]. Research in International Business and Finance, 2025, 75: 102771.

[16]. Bhattacharya I, Mickovic, A. Accounting fraud detection using contextual language learning[J]. International Journal of Accounting Information Systems, 2024, 53.

[17]. Fan S, Wu, Y, Yang, R. Measuring firm-level supply chain risk using a generative large language model[J]. Finance Research Letters, 2025, 77.

[18]. Zou Y, Shi, M, Chen, Z, et al. ESGReveal: An LLM-based approach for extracting structured data from ESG reports[J]. Journal of Cleaner Production, 2025, 489.

[19]. Zhao J, Wang, X J J o C A, Finance. Unleashing efficiency and insights: Exploring the potential applications and challenges of ChatGPT in accounting[J], 2024, 35(1): 269-276.

[20]. Street D, Wilck, J, Chism, Z. Six principles for the effective use of ChatGPT and other large language models in accounting[J]. CPA Journal, 2023.

[21]. Fotoh L, Mugwira, T. Exploring Large Language Models (ChatGPT) in External Audits: Implications and Ethical Considerations[J]. Available at SSRN 4453835, 2023.

[22]. Street D, Wilck, J. 'Let’s Have a Chat': Principles for the Effective Application of ChatGPT and Large Language Models in the Practice of Forensic Accounting[J]. Journal of Forensic Investigative Accounting, 2023.

[23]. Boritz J E, Stratopoulos, T C. AI and the accounting profession: Views from industry and academia[J]. Journal of Information Systems, 2023, 37(3): 1-9.

[24]. Dong M M, Stratopoulos, T C, Wang, V X. A scoping review of ChatGPT research in accounting and finance[J]. International Journal of Accounting Information Systems, 2024, 55: 100715.

[25]. Yi Z, Cao, X, Chen, Z, et al. Artificial intelligence in accounting and finance: Challenges and opportunities[J]. IEEE Access, 2023, 11: 129100-129123.

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