Digital Technologies Research and Applications

Review

Generative Artificial Intelligence in Finance: A Systematic Literature Review and a Research Agenda

Downloads

https://doi.org/10.54963/dtra.v4i2.1191
Chen, Y., Yan, S., Jin, Y., Li, J., & Jin, X. (2025). Generative Artificial Intelligence in Finance: A Systematic Literature Review and a Research Agenda. Digital Technologies Research and Applications, 4(2), 14–32. https://doi.org/10.54963/dtra.v4i2.1191
Volume 4 Issue 2: September 2025
Copyright (c) 2025 Yunfei Chen, Siyuan Yan, Yichen Jin, Jiayue Li, Xiyuan Jin
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright

The authors shall retain the copyright of their work but allow the Publisher to publish, copy, distribute, and convey the work.

License

Digital Technologies Research and Applications (DTRA)  publishes accepted manuscripts under Creative Commons Attribution 4.0 International (CC BY 4.0). Authors who submit their papers for publication by DTRA agree to have the CC BY 4.0 license applied to their work, and that anyone is allowed to reuse the article or part of it free of charge for any purpose, including commercial use. As long as the author and original source are properly cited, anyone may copy, redistribute, reuse, and transform the content.

Authors

  • Yunfei Chen

    School of Business, East China University of Science and Technology, Shanghai 200237, China
  • Siyuan Yan

    School of Business, East China University of Science and Technology, Shanghai 200237, China
  • Yichen Jin

    School of Business, East China University of Science and Technology, Shanghai 200237, China
  • Jiayue Li

    School of Business, East China University of Science and Technology, Shanghai 200237, China
  • Xiyuan Jin

    School of Business, East China University of Science and Technology, Shanghai 200237, China

Received: 23 April 2025; Revised: 6 June 2025; Accepted: 10 June 2025; Published: 24 June 2025

This study presents a systematic literature review on the implications of the use of generative artificial intelligence (GAI) in finance. With the rapid advancement of GAI technologies and hybrid adversarial‑variational frameworks, GAI’s integration into the financial industry has gained significant importance. Despite the growing body of research, comprehensive analyses of GAI’s potential applications, opportunities, and challenges in finance remain limited. The objective of our study is to synthesize the existing literature on the implications of GAI in finance and propose future research directions. The methodology involves a five‑step systematic literature review process, including identification, selection, relevance and quality assessment, data extraction, and data synthesis of relevant articles published between 2020 and 2025. The evaluation based on 42 selected articles highlights several applications of GAI in finance, which include synthetic data‑driven financial innovation, time‑series forecasting and algorithmic trading, risk modeling and stress testing, as well as GAI‑driven budgeting tools. Potential opportunities for GAI use in finance embrace enhanced operational efficiency, optimized customer service, innovation and sustainability capabilities, strengthened financial compliance, and improved data processing and analytical capabilities. Nevertheless, challenges such as technical risks, regulatory risks, ethics and moral concerns, market risks, operation and maintenance risks, and mental risks are also identified. Finally, we propose a research agenda focusing on both process‑related and content‑related recommendations to address these challenges and guide future research on the implications of GAI in finance.

Keywords:

GAI‑Alignment Risk Generative Artificial Intelligence Labor Displacement Model Hallucination Regulation

References

  1. Ho, J.; Jain, A.; Abbeel, P. Denoising Diffusion Probabilistic Models. Adv. Neural Inf. Process. Syst. 2020, 33, 6840–6851.
  2. Joshi, S. Using Gen AI Agents with GAN and VAE to Enhance Resilience of US Markets. Int. J. Comput. Sci. Inf. Technol. Control Eng. 2025, 12, 23–38. DOI: https://doi.org/10.5121/ijcsitce.2025.12102
  3. Vaswani, A.; Shazeer, N.; Parmar, N.; et al. Attention is All You Need. Adv. Neural Inf. Process. Syst. 2017, 30.
  4. Goodfellow, I.J.; Pouget-Abadie, J.; Mirza, M.; et al. Generative Adversarial Nets. Adv. Neural Inf. Process. Syst. 2014, 27, 2672–2680. DOI: https://doi.org/10.1007/978-3-658-40442-0_9
  5. Cao, L. AI in Finance: Challenges, Techniques, and Opportunities. ACM Comput. Surv. 2022, 55, 1–38. DOI: https://doi.org/10.1145/3502289
  6. Chou, H.M.; Cho, T.L. Utilizing Text Mining for Labeling Training Models from Futures Corpus in Generative AI. Appl. Sci. 2023, 13, 9622. DOI: https://doi.org/10.3390/app13179622
  7. Karst, F.S.; Li, M.M.; Leimeister, J.M. SynDEc: A Synthetic Data Ecosystem. Electron. Markets 2025, 35, 7. DOI: https://doi.org/10.1007/s12525-024-00746-8
  8. Kanbach, D.K.; Heiduk, L.; Blueher, G.; et al. The GenAI is out of the bottle: Generative artificial intelligence from a business model innovation perspective. Rev. Manag. Sci. 2024, 18, 1189–1220. DOI: https://doi.org/10.1007/s11846-023-00696-z
  9. Korinek, A. Generative AI for economic research: Use cases and implications for economists. J. Econ. Lit. 2023, 61, 1281–1317. DOI: https://doi.org/10.1257/jel.20231736
  10. Bhattacharya, R.; Aoun, M.A. Using Generative AI in Finance, and the Lack of Emergent Behavior in LLMs. Commun. ACM 2024, 67, 6–7. DOI: https://doi.org/10.1145/3674118
  11. Hui, X.; Reshef, O.; Zhou, L. The Short-Term Effects of Generative Artificial Intelligence on Employment: Evidence from an Online Labor Market. Organ. Sci. 2024, 35, 1977–1989. DOI: https://doi.org/10.1287/orsc.2023.18441
  12. Rombach, R.; Blattmann, A.; Lorenz, D.; et al. High-Resolution Image Synthesis with Latent Diffusion Models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA, 18–24 June 2022.
  13. Doshi-Velez, F.; Kim, B. Towards a Rigorous Science of Interpretable Machine Learning. arXiv preprint 2017. arXiv: 1702.08608v2.
  14. Qu, Y.; Ding, M.; Sun, N.; et al. The Frontier of Data Erasure: A Survey on Machine Unlearning for Large Language Models. Computer 2025, 58, 45–57. DOI: https://doi.org/10.1109/MC.2024.3405397
  15. López-Lira, A.; Tang, Y. Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models. arXiv preprint 2023. arXiv: 2304.07619.
  16. Leitner, G.; Singh, J.; van der Kraaij, A.; et al. The Rise of Artificial Intelligence: Benefits and Risks for Financial Stability. Financ. Stab. Rev. 2024, 1. Available form: https://www.fsb.org/uploads/P14112024.pdf
  17. Jovanovic, M.; Campbell, M. Generative Artificial Intelligence: Trends and Prospects. Computer 2022, 55, 107–112. DOI: https://doi.org/10.1109/MC.2022.3192720
  18. Devlin, J.; Chang, M.W.; Lee, K.; et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Minneapolis, MN, USA, 2–7 June 2019. DOI: https://doi.org/10.18653/v1/N19-1423
  19. Dosovitskiy, A.; Beyer, L.; Kolesnikov, A.; et al. An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. arXiv preprint 2020. arXiv: 2010.11929.
  20. Lim, B.; Ahmed, M.; Zohren, S. Temporal Fusion Transformers for Interpretable Multi-Horizon Time Series Forecasting. Int. J. Forecast. 2021, 37, 1748–1764. DOI: https://doi.org/10.1016/j.ijforecast.2021.03.012
  21. McMahan, B.; Moore, E.; Ramage, D.; et al. Communication-Efficient Learning of Deep Networks from Decentralized Data. In Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, Palau de Congressos, Valencia, Spain, 2–4 May 2024.
  22. Tkachenko, N. Opportunities for Synthetic Data in Nature and Climate Finance. Front. Artif. Intell. 2024, 6, 1168749. DOI: https://doi.org/10.3389/frai.2023.1168749
  23. Adiputra, I.N.M.; Lin, P.C.; Wanchai, P. The Effectiveness of Generative Adversarial Network-Based Oversampling Methods for Imbalanced Multi-Class Credit Score Classification. Electronics 2025, 14, 697. DOI: https://doi.org/10.3390/electronics14040697
  24. Zhu, M.; Gong, Y.; Xiang, Y.; et al. Utilizing GANs for Fraud Detection: Model Training with Synthetic Transaction Data. arXiv preprint 2024. arXiv: 2402.09830.
  25. Chiu, I.C.; Hung, M.W. Finance-Specific Large Language Models: Advancing Sentiment Analysis and Return Prediction with LLaMA 2. Pac. Basin Financ. J. 2025, 90, 102632. DOI: https://doi.org/10.1016/j.pacfin.2024.102632
  26. Iaroshev, I.; Pillai, R.; Vaglietti, L.; et al. Evaluating Retrieval-Augmented Generation Models for Financial Report Question and Answering. Appl. Sci. 2024, 14, 9318. DOI: https://doi.org/10.3390/app14209318
  27. Brunnermeier, M.K.; Pedersen, L.H. Market Liquidity and Funding Liquidity. Rev. Financ. Stud. 2009, 22, 2201–2238. DOI: https://doi.org/10.1093/rfs/hhn098
  28. Qin, J., Qin, Q. Cloud Platform for Enterprise Financial Budget Management based on Artificial Intelligence.Wirel. Commun. Mob. Com. 2021, 2021, 8038433. DOI: https://doi.org/10.1155/2021/8038433
  29. Chen, Y., Biswas, M.I. Turning Crisis into Opportunities: How a Firm can Enrich its Business Operations Using Artificial Intelligence and Big Data during COVID-19. Sustainability 2021, 13, 12656. DOI: https://doi.org/10.3390/su132212656
  30. Sadiq, R.B.; Safie, N.; Abd Rahman, A.H.; et al. Artificial Intelligence Maturity Model: A Systematic Literature Review. PeerJ Comput. Sci. 2021, 7, e661. DOI: https://doi.org/10.7717/peerj-cs.661
  31. Zuiderwijk, A.; Chen, Y.C.; Salem, F. Implications of the Use of Artificial Intelligence in Public Governance: A Systematic Literature Review and a Research Agenda. Gov. Inf. Q. 2021, 38, 101577. DOI: https://doi.org/10.1016/j.giq.2021.101577
  32. Sai, S.; Arunakar, K.; Chamola, V.; et al. Generative AI for Finance: Applications, Case Studies and Challenges. Expert Syst. 2025, 42, e70018. DOI: https://doi.org/10.1111/exsy.70018
  33. Mattusch, M. Generative AI for European Asset Pricing: Alleviating the Momentum Anomaly. Eur. J. Financ. 2024, 1–39. DOI: https://doi.org/10.1080/1351847X.2024.2439979
  34. Roychowdhury, S. Journey of Hallucination-Minimized Generative AI Solutions for Financial Decision Makers. In Proceedings of the 17th ACM International Conference on Web Search and Data Mining, Mérida, México, 4–8 March 2024. DOI: https://doi.org/10.1145/3616855.3635737
  35. Anica-Popa, I.F.; Vrîncianu, M.; Anica-Popa, L.E.; et al. Framework for Integrating Generative AI in Developing Competencies for Accounting and Audit Professionals. Electronics 2024, 13, 2621. DOI: https://doi.org/10.3390/electronics13132621
  36. Kshetri, N.; Dwivedi, Y.K.; Davenport, T.H.; et al. Generative Artificial Intelligence in Marketing: Applications, Opportunities, Challenges, and Research Agenda. Int. J. Inf. Manag. 2024, 75, 102716. DOI: https://doi.org/10.1016/j.ijinfomgt.2023.102716
  37. Castelnovo, A.; Depalmas, R.; Mercorio, F.; et al. Augmenting XAI with LLMs: A Case Study in Banking Marketing Recommendation. In World Conference on Explainable Artificial Intelligence., Valletta, Malta, 17–19 July 2024. DOI: https://doi.org/10.1007/978-3-031-63787-2_11
  38. Jackson, I.; Ivanov, D.; Dolgui, A.; et al. Generative Artificial Intelligence in Supply Chain and Operations Management: A Capability-Based Framework for Analysis and Implementation. Int. J. Prod. Res. 2024, 62, 6120–6145. DOI: https://doi.org/10.1080/00207543.2024.2309309
  39. Chakraborty, D.; Kar, A.K.; Patre, S.; et al. Enhancing Trust in Online Grocery Shopping through Generative AI Chatbots. J. Bus. Res. 2024, 180, 114737. DOI: https://doi.org/10.1016/j.jbusres.2024.114737
  40. Kshetri, N. Generative Artificial Intelligence in the Financial Services Industry. Computer 2024, 57, 102–108. DOI: https://doi.org/10.1109/MC.2024.3382452
  41. Oder, N.; Béland, D. Artificial Intelligence, Emotional Labor, and the Quest for Sociological and Political Imagination Among Low-Skilled Workers. Policy Soc. 2024, 44, 116–128. DOI: https://doi.org/10.1093/polsoc/puae034
  42. Kliestik, T.; Dragomir, R.; Băluță, A.V.; et al. Enterprise Generative Artificial Intelligence Technologies, Internet of Things and Blockchain-Based Fintech Management, and Digital Twin Industrial Metaverse in the Cognitive Algorithmic Economy. Oecon. Copernic. 2024, 15, 1183–1221. DOI: https://doi.org/10.24136/oc.3109
  43. Pantano, E.; Serravalle, F.; Priporas, C.V. The Form of AI-Driven Luxury: How Generative AI (GAI) and Large Language Models (LLMs) Are Transforming the Creative Process. J. Mark. Manag. 2024, 40, 1771–1790. DOI: https://doi.org/10.1080/0267257X.2024.2436096
  44. Zada, M.; Khan, S.; Mehmood, S.; et al. Generative Artificial Intelligence in FinTech: Applications, Environmental, Social, and Governance Considerations, and Organizational Performance: The Moderating Role of Ethical Dilemmas. Oecon. Copernic. 2024, 15, 1303–1347. DOI: https://doi.org/10.24136/oc.3323
  45. Alnahhas, N.; Yousef, D. GAI as a Catalyst in National Technology Sovereignty: Evaluating the Influence of GAI on Government Policy. In Proceedings of the 25th Annual International Conference on Digital Government Research, Taipei, China, 11–14 June 2024. DOI: https://doi.org/10.1145/3657054.3657126
  46. Wolfe, R.; Mitra, T. The Impact and Opportunities of Generative AI in Fact-Checking: An Interview Study. In Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency, Rio de Janeiro, Brazil, 3–6 June 2024. DOI: https://doi.org/10.1145/3630106.3658987
  47. Khan, M.S.; Umer, H. ChatGPT in Finance: Applications, Challenges, and Solutions. Heliyon 2024, 10. DOI: https://doi.org/10.1016/j.heliyon.2024.e24890
  48. Lazaroiu, G.; Gedeon, T.; Rogalska, E.; et al. Digital Twin-Based Cyber-Physical Manufacturing Systems, Extended Reality Metaverse Enterprise and Production Management Algorithms, and Internet of Things Financial and Labor Market Technologies in Generative Artificial Intelligence Economics. Oecon. Copernic. 2024, 15, 837–870. DOI: https://doi.org/10.24136/oc.3183
  49. Tkachenko, N.; Frieder, S.; Griffiths, R.R.; et al. Analyzing Global Utilization and Missed Opportunities in Debt-for-Nature Swaps with Generative AI. Front. Artif. Intell. 2024, 7, 1167137. DOI: https://doi.org/10.3389/frai.2024.1167137
  50. Walkowiak, E. Task-Interdependencies between Generative AI and Workers. Econ. Lett. 2023, 231, 111315. DOI: https://doi.org/10.1016/j.econlet.2023.111315
  51. Onan, A.; Dursun, E.D. Benchmarking Retrieval Augmented Generation in Quantitative Finance. In International Conference on Intelligent and Fuzzy Systems, Canakkale, Türkiye, 16–18 July 2024. DOI: https://doi.org/10.1007/978-3-031-67195-1_9
  52. Dong, M.M.; Stratopoulos, T.C.; Wang, V.X. A Scoping Review of ChatGPT Research in Accounting and Finance. Int. J. Account. Inf. Syst. 2024, 55, 100715. DOI: https://doi.org/10.1016/j.accinf.2024.100715
  53. Akhavan, A.; Jalali, M.S. Generative AI and Simulation Modeling: How Should You (Not) Use Large Language Models Like ChatGPT. Syst. Dyn. Rev. 2024, 40, e1773. DOI: https://doi.org/10.1002/sdr.1773
  54. Lee, J.; Stevens, N.; Han, S.C. Large Language Models in Finance (FinLLMs). Neural Comput. Appl. 2025, 1–15. DOI: https://doi.org/10.1007/s00521-024-10495-6
  55. Chowdhury, S.; Budhwar, P.; Wood, G. Generative Artificial Intelligence in Business: Towards a Strategic Human Resource Management Framework. Br. J. Manag. 2024, 35, 1680–1691. DOI: https://doi.org/10.1111/1467-8551.12824
  56. De Villiers, C.; Dimes, R.; Molinari, M. How Will AI Text Generation and Processing Impact Sustainability Reporting? Critical Analysis, a Conceptual Framework and Avenues for Future Research. Sustain. Account. Manag. Policy J. 2024, 15, 96–118. DOI: https://doi.org/10.1108/SAMPJ-02-2023-0097
  57. Taeihagh, A. Governance of Generative AI. Policy Soc. 2025, 44, 1–22. DOI: https://doi.org/10.1093/polsoc/puaf001
  58. Campbell, M.; Jovanović, M. Disinfecting AI: Mitigating Generative AI’s Top Risks. Computer 2024, 57, 111–116. DOI: https://doi.org/10.1109/MC.2024.3374433
  59. Beltran, M.A.; Ruiz Mondragon, M.I.; Han, S.H. Comparative Analysis of Generative AI Risks in the Public Sector. In Proceedings of the 25th Annual International Conference on Digital Government Research, Taipei, China, 11–14 June 2024. DOI: https://doi.org/10.1145/3657054.3657125
  60. Dong, H.; Chen, J. Meta-Regulation: An Ideal Alternative to the Primary Responsibility as the Regulatory Model of Generative AI in China. Comput. Law Secur. Rev. 2024, 54, 106016. DOI: https://doi.org/10.1016/j.clsr.2024.106016
  61. Lee, D.K.C.; Guan, C.; Yu, Y.; et al. A Comprehensive Review of Generative AI in Finance. FinTech 2024, 3, 460–478. DOI: https://doi.org/10.3390/fintech3030025
  62. Kang, H., Liu, X.Y. Deficiency of Large Language Models in Finance: An Empirical Examination of Hallucination. arxiv preprint 2023. arxiv:2311.15548.
  63. Siddik, A.B.; Li, Y.; Du, A.M.; et al. Fueling Financial Development: The Crucial Role of Generative AI Financing Across Nations. Financ. Res. Lett. 2025, 72, 106519. DOI: https://doi.org/10.1016/j.frl.2024.106519
  64. Budhwar, P.; Chowdhury, S.; Wood, G.; Aguinis, H.; Bamber, G.J.; Beltran, J.R.; et al. Human Resource Management in the Age of Generative Artificial Intelligence: Perspectives and Research Directions on ChatGPT. Hum. Resour. Manag. J. 2023, 33, 606–659. DOI: https://doi.org/10.1111/1748-8583.12524
  65. Zhu, H.; Vigren, O.; Söderberg, I.L. Implementing Artificial Intelligence Empowered Financial Advisory Services: A Literature Review and Critical Research Agenda. J. Bus. Res. 2024, 174, 114494. DOI: https://doi.org/10.1016/j.jbusres.2023.114494
  66. Chatterjee, P.; Das, D.; Rawat, D.B. A Generative AI Approach for Ensuring Data Integrity Security Resilience in Fintech Systems. In Proceedings of the 2024 IEEE 24th International Symposium on Cluster, Cloud and Internet Computing Workshops, Philadelphia, PA, USA, 6–9 May 2024. DOI: https://doi.org/10.1109/CCGridW63211.2024.00027