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Title: | 應用生成式人工智慧於銀行財富管理業務之研究-以H銀行為例 Application of generative artificial intelligence in banking wealth management-A case study of H bank |
Authors: | 游仁政 Yu, Jen-Cheng |
Contributors: | 韓傳祥 Han, Chuan-Hsiang 游仁政 Yu, Jen-Cheng |
Keywords: | 生成式人工智慧 財富管理 Generative AI Wealth management |
Date: | 2025 |
Issue Date: | 2025-09-01 15:30:15 (UTC+8) |
Abstract: | 生成式人工智慧(Generative AI,簡稱 GenAI)正加速金融業數位轉型,財富管理為其優先應用領域之一。本研究以尚未於財富管理業務導入 GenAI 的 H 銀行為例,透過問卷調查該行理財人員與主管(153 份樣本),並訪談兩位具相關實務經驗的金融科技專家,從操作實務與技術規劃面向,探討 GenAI 的應用潛力與導入挑戰。研究結果顯示,客戶資料彙整與需求萃取、基金保險商品與稅務法令/作業規範查詢,以及話術模擬訓練等情境,最獲受訪者認同。受訪者普遍持審慎樂觀態度,認為 GenAI 在涉及交易建議時仍須保留人工審閱機制,並強調可解釋性與教育訓練對提升應用效率的關鍵角色。導入挑戰則集中於資料整合與技術部署,信任建立亦為重要前提,建議採分階段導入,並同步完善配套措施。 Generative Artificial Intelligence is accelerating the digital transformation of the financial industry, with wealth management identified as one of its key application domains. This study focuses on H Bank, which has not yet implemented GenAI in its wealth management operations. Through a survey of 153 financial advisors and supervisors, complemented by in-depth interviews with two fintech experts with relevant implementation experience, this research explores the application potential and implementation challenges of GenAI from both operational and technical perspectives. The findings indicate that use cases such as customer data consolidation and demand extraction, product and regulatory information retrieval(e.g., funds, insurance, tax laws), and sales script simulation are widely recognized by respondents. While participants expressed cautious optimism, they emphasized the need for human review in transaction-related scenarios and highlighted explainability and training as critical to enhancing usage effectiveness. Key implementation challenges include data integration and technical deployment, while trust-building remains a fundamental prerequisite. The study recommends a phased approach to adoption, accompanied by supporting measures to ensure successful implementation. |
Reference: | 中文參考文獻 工商時報(2025年2月3日)。4公股銀行 強攻AI運用。https://www.ctee.com.tw/news/20250203700118-439901 數位時代(2024年11月1日)。2024台北金融科技展登場!金控猛攻AI財管商機,FinTech有何三大亮點趨勢?https://www.bnext.com.tw/article/81112/fintech-taipei-2024?utm_source=chatgpt.com 遠見雜誌(2024年5月28日)。生成式AI如何影響金融業?麥肯錫:銀行業利潤將因此增長600億。https://www.gvm.com.tw/article/113080 金融監督管理委員會(2024年6月20日)。金融業運用人工智慧(AI)指引。https://www.fsc.gov.tw/ch/home.jsp?id=96&parentpath=0,2&mcustomize=news_view.jsp&dataserno=202406200001&dtable=News 金融監督管理委員會(2025年5月27日)。亞洲資產管理中心政策之推動成果。https://www.fsc.gov.tw/ch/home.jsp?dataserno=202505270001&dtable=News&id=96&mcustomize=news_view.jsp&parentpath=0,2
英文參考文獻 Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 610–623. https://doi.org/10.1145/3442188.3445922 Christiano, P., Leike, J., Brown, T., Martic, M., Legg, S., & Amodei, D. (2017). Deep reinforcement learning from human preferences. arXiv. https://arxiv.org/abs/1706.03741 Davenport, T., & Alavi, M. (2023, July 6). How to train generative AI using your company’s data. Harvard Business Review. https://hbr.org/2023/07/how-to-train-generative-ai-using-your-companys-data Evident. (2024, October). Evident AI Index: Benchmarking the AI capabilities of major banks. https://evidentinsights.com/ai-index/ European Commission. (2019). Ethics Guidelines for Trustworthy AI. https://digital-strategy.ec.europa.eu/en/library/ethics-guidelines-trustworthy-ai European Commission. (2020). Assessment List for Trustworthy Artificial Intelligence (ALTAI) for self-assessment. https://digital-strategy.ec.europa.eu/en/library/assessment-list-trustworthy-artificial-intelligence-altai-self-assessment European Commission.(2024). Regulatory framework proposal on Artificial Intelligence. https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai Hadji Misheva, B., Osterrieder, J., Hirsa, A., Kulkarni, O., & Lin, S. F. (2021). Explainable AI in credit risk management. arXiv. https://arxiv.org/abs/2103.00949 International Monetary Fund. (2024, October). Global financial stability report: Steadying the course. https://www.imf.org/en/Publications/GFSR/Issues/2024/10/22/global-financial-stability-report-october-2024 J.P. Morgan. (2024, July 22). Quest IndexGPT: Harnessing generative AI for investable indices. https://www.jpmorgan.com/insights/markets/indices/indexgpt Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., & Fung, P. (2023). Survey of hallucination in natural language generation. arXiv. https://arxiv.org/abs/2202.03629 Kang, H., Hu, S., Wang, C., & Wu, Y. (2023). Hallucination in financial large language models: An empirical analysis and mitigation strategies. arXiv. https://arxiv.org/abs/2311.15548 Kaneko, M., & Baldwin, T. (2024). A Little Leak Will Sink a Great Ship: Survey of Transparency for Large Language Models from Start to Finish. arXiv. https://arxiv.org/abs/2403.16139 Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., ... & Riedel, S. (2020). Retrieval-augmented generation for knowledge-intensive NLP tasks. Advances in Neural Information Processing Systems. https://proceedings.neurips.cc/paper/2020/file/6b493230205f780e1bc26945df7481e5-Paper.pdf Li, L., Chang, T.-Y., & Wang, H. (2024). Multimodal Gen-AI for fundamental investment research. arXiv. https://arxiv.org/abs/2401.06164 Lo, A. W., & Ross, J. (2024). Can ChatGPT plan your retirement?: How large language models can evolve to provide trusted financial advice. SSRN. https://dx.doi.org/10.2139/ssrn.4722780 Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774. https://papers.nips.cc/paper/7062-a-unified-approach-to-interpreting-model-predictions Market Research.biz. (2024, March). Generative AI in Wealth Management Market. https://marketresearch.biz/report/generative-ai-in-wealth-management-market/?utm_source=chatgpt.com McKinsey & Company. (2023, June 14). The economic potential of generative AI: The next productivity frontier. https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-economic-potential-of-generative-ai-the-next-productivity-frontier#key-insights Morgan Stanley. (2024, October 23). Morgan Stanley Research announces AskResearchGPT. https://www.morganstanley.com/press-releases/morgan-stanley-research-announces-askresearchgpt Morgan Stanley. (2024, June 26). AI at Morgan Stanley: Debrief Launch. https://www.morganstanley.com/press-releases/ai-at-morgan-stanley-debrief-launch OpenAI. (2022, November 30). Introducing ChatGPT. https://openai.com/index/chatgpt/ OpenAI. (2023). GPT-4 technical report. arXiv. https://arxiv.org/abs/2303.08774 O’Neil, C. (2016). Weapons of math destruction. Crown Publishing. Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). Why should I trust you?: Explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1135–1144). https://doi.org/10.1145/2939672.2939778 Romanko, O., Narayan, A., & Kwon, R. H. (2023). ChatGPT-based investment portfolio selection. arXiv. https://arxiv.org/abs/2308.06260 Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. arXiv. https://arxiv.org/abs/1706.03762 |
Description: | 碩士 國立政治大學 國際金融碩士學位學程 112ZB1044 |
Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0112ZB1044 |
Data Type: | thesis |
Appears in Collections: | [國際金融碩士學位學程] 學位論文
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