AI in Finance: Leveraging Large Language Models for Enhanced Decision-Making and Risk Management
DOI:
https://doi.org/10.5281/zenodo.13299843Keywords:
artificial intelligence, financial services, large language models, natural language processing, risk managementAbstract
This paper explores the transformative potential of Large Language Models (LLMs) in the financial sector, focusing on their applications in enhancing decision-making, risk management, and customer service. It highlights the significant benefits of LLMs, such as increased efficiency, accuracy, and scalability, while addressing the technical, ethical, and regulatory challenges associated with their deployment. Key challenges include data integration, model training, bias mitigation, transparency, and regulatory compliance. The paper also discusses future directions, emphasizing the need for advancements in AI explainability, fairness, and robustness, as well as interdisciplinary research and collaboration. Successfully addressing these challenges will enable financial institutions to harness the full potential of LLMs, driving innovation and improving operational efficiency and client services.
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References
Abbasiantaeb, Zahra, et al. (2024). Let the llms talk: Simulating human-to-human conversational qa via zero-shot llm-to-llm interactions. Proceedings of the 17th ACM International Conference on Web Search and Data Mining.
Shuyao He, Yue Zhu, Yushan Dong, Hao Qin, & Yuhong Mo. (2024). Lidar and monocular sensor fusion depth estimation. Applied Science and Engineering Journal for Advanced Research, 3(3), 20–26. https://doi.org/10.5281/zenodo.11347309.
Valmeekam, Karthik, et al. (2022). Large language models still can't plan (a benchmark for LLMs on planning and reasoning about change). NeurIPS 2022 Foundation Models for Decision Making Workshop.
Vaswani, Ashish, et al. (2017). Attention is all you need. Advances in neural information processing systems, 30.
Mo, Y. ., Qin, H., Dong, Y., Zhu, Z., & Li, Z. (2024). Large Language Model (LLM) AI text generation detection based on transformer deep learning algorithm. International Journal of Engineering and Management Research, 14(2), 154–159. https://doi.org/10.5281/zenodo.11124440.
Dai, S., Dai, J., Zhong, Y., Zuo, T., & Mo, Y. (2024). The cloud-based design of unmanned constant temperature food delivery trolley in the context of artificial intelligence. Journal of Computer Technology and Applied Mathematics, 1(1), 6–12.
Zheng Lin, Zeyu Wang, Yue Zhu, Zichao Li, & Hao Qin. (2024). Text sentiment detection and classification based on integrated learning algorithm. Applied Science and Engineering Journal for Advanced Research, 3(3), 27–33. https://doi.org/10.5281/zenodo.11516191.
Zamfirescu-Pereira, J. D., et al. (2023). Why Johnny can’t prompt: how non-AI experts try (and fail) to design LLM prompts. Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems.
Qin, H., (2024). Transforming education with large language models: Opportunities, challenges, and ethical considerations. Preprint. doi:10.13140/RG.2.2.16976.52488.
Zeyu Wang, Yue Zhu, Zichao Li, Zhuoyue Wang, Hao Qin, & Xinqi Liu. (2024). Graph neural network recommendation system for football formation. Applied Science and Biotechnology Journal for Advanced Research, 3(3), 33–39. https://doi.org/10.5281/zenodo.12198843.
Zhao, W., Liu, X., Xu, R., Xiao, L., & Li, M. (2024). E-commerce webpage recommendation scheme base on semantic mining and neural networks. Journal of Theory and Practice of Engineering Science, 4(03), 207–215. https://doi.org/10.53469/jtpes.2024.04(03).20
Li, Z. ., Yu, H., Xu, J., Liu, J., & Mo, Y. (2023). Stock market analysis and prediction using LSTM: A case study on technology stocks. Innovations in Applied Engineering and Technology, 2(1), 1–6.
Hao Qin. (2024). Revolutionizing cryptocurrency operations: The role of domain-specific large language models (LLMs). International Journal of Computer Trends and Technology, 72(6), 101-113.
Li, S., Mo, Y., & Li, Z. (2022). Automated pneumonia detection in chest x-ray images using deep learning model. Innovations in Applied Engineering and Technology, 1(1), 1–6.
Haoran Yu, Chang Yu, Zihan Wang, Dongxian Zou, & Hao Qin. (2024). Enhancing healthcare through large language models: A study on medical questions answering, Proceedings to 2024 IEEE 6th International Conference on Power, Intelligent Computing and Systems (ICPICS).
Hao Qin, & Li, Z. (2024). Precision in practice: Enhancing healthcare with domain-specific language models. Applied Science and Engineering Journal for Advanced Research, 3(4), 28–33. https://doi.org/10.5281/zenodo.13253336.
Alberts, Ian L., et al. (2023). Large language models (LLM) and ChatGPT: What will the impact on nuclear medicine be?. European Journal of Nuclear Medicine and Molecular Imaging, 50(6), 1549-1552.
Mizrahi, Moran, et al. (2024). State of what art? A call for multi-prompt llm evaluation. Transactions of the Association for Computational Linguistics, 12, 933-949.
Hao Qin, & Zhi Li. (2024). A study on enhancing government efficiency and public trust: The transformative role of artificial intelligence and large language models. International Journal of Engineering and Management Research, 14(3), 57–61. https://doi.org/10.5281/zenodo.12619360.
Yuhong Mo, Chaoyi Tan, Chenghao Wang, Hao Qin, & Yushan Dong. (2024). Make scale invariant feature transform “Fly” with CUDA. International Journal of Engineering and Management Research, 14(3), 38–45. https://doi.org/10.5281/zenodo.11516606.
Zhi Li. (2024). Ethical frontiers in artificial intelligence: navigating the complexities of bias, privacy, and accountability. International Journal of Engineering and Management Research, 14(3), 109–116. https://doi.org/10.5281/zenodo.12792741.
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Copyright (c) 2024 Alec Goldberg
This work is licensed under a Creative Commons Attribution 4.0 International License.
