| dc.contributor.advisor | 郭維裕 | zh_TW |
| dc.contributor.advisor | Kuo, Wei-Yu | en_US |
| dc.contributor.author (Authors) | 虞以臻 | zh_TW |
| dc.contributor.author (Authors) | Yu, I-Chen | en_US |
| dc.creator (作者) | 虞以臻 | zh_TW |
| dc.creator (作者) | Yu, I-Chen | en_US |
| dc.date (日期) | 2025 | en_US |
| dc.date.accessioned | 1-Sep-2025 15:29:59 (UTC+8) | - |
| dc.date.available | 1-Sep-2025 15:29:59 (UTC+8) | - |
| dc.date.issued (上傳時間) | 1-Sep-2025 15:29:59 (UTC+8) | - |
| dc.identifier (Other Identifiers) | G0112ZB1023 | en_US |
| dc.identifier.uri (URI) | https://nccur.lib.nccu.edu.tw/handle/140.119/159175 | - |
| dc.description (描述) | 碩士 | zh_TW |
| dc.description (描述) | 國立政治大學 | zh_TW |
| dc.description (描述) | 國際金融碩士學位學程 | zh_TW |
| dc.description (描述) | 112ZB1023 | zh_TW |
| dc.description.abstract (摘要) | 根據麥肯錫研究指出，金融產業每年透過AI創造的產值高達1兆美元。傳統銀行以分行為核心的經營模式，在金融科技衝擊下將逐漸式微，銀行應善用人工智慧技術加速創新，爭取數位通路客戶。本研究旨在探討台灣銀行業AI應用現況，並借鑑美國、新加坡經驗提出發展建議。報告分析JPMorgan Chase、Morgan Stanley及星展銀行等標竿機構實務案例，探討AI技術在貸款審核、智能客服、風險管理等核心業務的應用成效。
本研究發現，各國監理策略呈現多元樣貌：美國採原則導向監管，重視創新自由與透明度；新加坡以金管局主導，透過產官合作推動負責任AI應用；台灣金管會發布AI指引，目前87%銀行業者已啟動AI應用計劃，但自動化決策程度仍偏保守。最後本研究對台灣銀行業提出五項建議：建立內部AI治理框架，確保模型透明性；發展AI審核模型，提升授信效率；拓展異業合作，建構金融生態圈；培育跨領域人才；成立AI研發聯盟，優先發展風險管理與反洗錢模型。期能為國內銀行數位轉型提供實務指引，在確保金融穩定的前提下加速AI應用，提升國際競爭力。 | zh_TW |
| dc.description.abstract (摘要) | According to McKinsey research, the financial industry creates up to $1 trillion in value annually through AI. The traditional branch-centric business model of banks is gradually declining under the impact of Fintech, and banks should leverage AI technology to accelerate innovation and attract digital channel customers. This research aims to explore the current state of AI applications in Taiwan's banking industry and propose development recommendations by drawing from the experiences of the United States and Singapore. The report analyzes practical cases from benchmark institutions such as JPMorgan Chase, Morgan Stanley, and DBS Bank, examining the effectiveness of AI technology applications in core business areas including loan approval, intelligent customer service, and risk management.
The study found that regulatory strategies across countries present diverse approaches: the United States adopts principle-based regulation, emphasizing innovation freedom and transparency; Singapore is led by the Monetary Authority of Singapore (MAS), promoting responsible AI applications through public-private cooperation; Taiwan's Financial Supervisory Commission (FSC) has issued AI guidelines, with 87% of banking institutions having initiated AI application plans, though the degree of automated decision-making remains conservative. Finally, this research proposes five recommendations for Taiwan's banking industry: establish internal AI governance frameworks to ensure model transparency; develop AI review/approval models to enhance credit approval efficiency; expand cross-industry collaboration to build financial ecosystems; cultivate interdisciplinary talent; and establish AI R&D alliances, prioritizing the development of risk management and anti-money laundering models. The aim is to provide practical guidance for domestic banks' digital transformation, accelerating AI applications while ensuring financial stability and enhancing international competitiveness. | en_US |
| dc.description.tableofcontents | 第一章 緒論 1
第一節 研究背景 1
第二節 研究動機與目的 4
第二章 人工智慧於金融應用的發展 6
第一節 金融業人工智慧應用 8
第二節 法遵科技 11
第三節 智能助理 14
第三章 案例探討 16
第一節 美國案例 17
第二節 新加坡案例 26
第四章 我國落實方法及執行情形 38
第一節 金融業運用人工智慧（AI）指引 38
第二節 我國發展現況 40
第五章 結論與建議 45
參考文獻 49 | zh_TW |
| dc.format.extent | 2497649 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#G0112ZB1023 | en_US |
| dc.subject (關鍵詞) | 數位轉型 | zh_TW |
| dc.subject (關鍵詞) | 人工智慧 | zh_TW |
| dc.subject (關鍵詞) | 銀行業AI應用 | zh_TW |
| dc.subject (關鍵詞) | Digital transformation | en_US |
| dc.subject (關鍵詞) | Artificial intelligence | en_US |
| dc.subject (關鍵詞) | Banking AI applications | en_US |
| dc.title (題名) | 人工智慧於銀行業的應用 | zh_TW |
| dc.title (題名) | Utilizing Artificial Intelligence in Banking | en_US |
| dc.type (資料類型) | thesis | en_US |
| dc.relation.reference (參考文獻) | 中文參考文獻
書籍/報告
1.詹文男、李震華、周維忠、王義智、數位轉型團隊（2022）。數位轉型力。商周出版社。144-146頁。
2.羅賓．斯普蘭（2024）。星展銀行數位轉型實踐手冊：世界最佳銀行是怎麼煉成的？星展執行長親揭成功心法（陳勁、龐寧婧譯）。商業周刊。
期刊
3.王煦棋（2024）。金融業AI監管的十字路口－從風控角度談臺灣AI法制，當代法律，28期，頁26–34。
4.許嘉芳（2024）。簡評美國白宮「安全、可靠且值得信賴的開發及使用AI總統行政命令」對AI治理發展之影響，科技法律透析，36卷4期，頁50–65。
5.蕭幸金、李興漢、丁小宇（2022）。法遵科技系統之規畫－以銀行業為例，電腦稽核，45期，頁19–42。
機構文件
6.台灣金融研訓院（2022）。我國銀行業2022年金融科技創新與數位轉型大調查。
7.台灣金融研訓院（2024）。我國銀行業面對生成式人工智慧的影響與因應。
8.台灣金融研訓院（2024）。我國銀行業2024年金融創新與數位轉型大調查。
9.金管會（2023）。金融科技發展路徑圖(2.0)。
10.金管會（2023）。金融業運用人工智慧(AI)之核心原則及政策。
11.金管會（2024）。金融業運用人工智慧(AI)指引。
12.金管會（2025）。金管會公布金融業應用人工智慧(AI)調查結果新聞稿。
13.中華民國銀行商業同業公會全國聯合會（2023）。金融機構運用人工智慧技術作業規範。
網路資料
14.中國信託商業銀行（2024年11月7日）。中國信託銀行致力綠色金融科技合作創新，攜手新加坡創新公司Evercomm研發AI碳金融管理平臺。https://www.ctbcholding.com/content/dam/twhoo/file/news/2024/20241107.pdf
15.台北富邦金控（2024年2月6日）。年關近慎防詐騙！北富銀AI洗防系統「獵鷹系統」正式啟用。https://www.fubon.com/financialholdings/news/news_1240206_215820.htm
16.台北富邦金控（2024年3月27日）。政府、銀行總動員AI科技助陣「鷹眼識詐聯盟」啟動 守護民眾財產！https://www.cw.com.tw/article/5129734
17.李昀璇（2023年9月20日）。實踐AI治理原則國泰金控聚焦四大技術。iThome.com。https://www.ithome.com.tw/news/158850
18.李昀璇（2025年5月21日）。國泰金控要靠自建金融知識LLM，打造臺灣首個金融AI Agent。iThome.com。https://www.ithome.com.tw/news/169082
19.李昀璇（2024年4月26日）。玉山怎麼用GenAI？12項應用服務大解析。iThome.com。https://www.ithome.com.tw/news/162551
20.周峻佑（2025年5月5日）。臺灣第一座防詐實驗室正式掛牌上路，玉山金揭金融無塵室發展方向。iThome.com。https://www.ithome.com.tw/news/168745
21.星展銀行（2021年9月2日）。【DBS客座文章】星展銀行如何打造大數據平台。iThome.com。https://www.ithome.com.tw/news/146484
22.國泰金控（2024年11月8日）。國泰生成式AI技術發展框架「GAIA」重磅發布！https://www.cathayholdings.com/holdings/brand/fintech/ctc/trends/2024cathay_gaia
23.國泰金控（2025年5月21日）。國泰金控採用NVIDIA NeMo平台為台灣打造金融代理。https://www.cathayholdings.com/holdings/lastest_news/news_archive/newsarticle?newsID=OBG6TQgqC0aWyxFawxX_rg
24.林勁傑（2025年5月21日）。玉山金導入NVIDIA H100試驗防詐預警效能提升880倍。經濟日報。https://money.udn.com/money/story/5613/8755777
25.鄭宇渟（2024年8月9日）。台新銀攜OneDegree驗證「台新腦」打造負責任AI系統。數位時代。https://www.digitimes.com.tw/tech/dt/n/shwnws.asp?id=0000699380_QXI28WVO92QNSLLI7K5
26.蘇文彬（2024年8月14日）。35家金融業者組聯盟用AI模型偵測警示帳戶攔阻詐騙金流，下一步要用聯合學習精進模型。iThome.com。https://www.ithome.com.tw/news/164486
27.凱基商業銀行（2025年6月5日）。房屋貸款流程有哪些？房貸要申請多久？常見房貸流程疑問大解析！https://www.kgibank.com.tw/zh-tw/kgibarticleshome/loan/ml-process
英文參考文獻
書籍
28.Arnone, G., & SpringerLink. (2024). AI and chatbots in Fintech: revolutionizing digital experiences and predictive analytics. Springer Nature Switzerland.
29.King, B. (2018). Bank 4.0: Banking everywhere, never at a bank. John Wiley & Sons.
期刊
30.Kálmán, J. (2025). The Role of Regulatory Sandboxes in FinTech Innovation: A Comparative Case Study of the UK, Singapore, and Hungary. FinTech, 4(2), 26.
31.Remolina, N. (2025). AI governance and algorithmic auditing in financial institutions: Lessons from Singapore. Journal of Risk Management in Financial Institutions, 18(3), 261–275.
32.Tulsi, K., Dutta, A., Singh, N., & Jain, D. (2024). Transforming financial services: The impact of AI on JP Morgan Chase's operational efficiency and decision-making. International Journal of Scientific Research & Engineering Trends, 10(1), 207–2013.
機構文件
33.Biswas, S., Carson, B., Chung, V., Singh, S., & Thomas, R. (2020). AI-bank of the future: Can banks meet the AI challenge? McKinsey & Company.
34.Maslej, N., et al. (2024). Artificial Intelligence Index Report 2024, Human-Centered Artificial Intelligence. United States of America. Retrieved from
35.Monetary Authority of Singapore. (2016). Fintech Regulatory Sandbox Guidelines.
36.Monetary Authority of Singapore. (2019). MAS Consults on Proposed Enhancements to Technology Risk and Business Continuity Management Guidelines.
37.Monetary Authority of Singapore. (2020). Sandbox Express.
38.Monetary Authority of Singapore. (2023). Veritas initiative. Monetary Authority of Singapore.
39.Monetary Authority of Singapore. (2025, April 17). MAS' Experts Panel Proposes Ways to Enhance Technology Resilience and Tackle Emerging Threats.
40.Stanford University, Institute for Human-Centered Artificial Intelligence. (2024). Artificial intelligence index report 2024. Stanford University.
41.White House Office of Science and Technology Policy. (2022). Blueprint for an AI Bill of Rights: Making automated systems work for the American people. The White House.
42.World Economic Forum. (2025). Global Risks Report 2025.
網路資料
43.Amazon Web Services, Inc. (2024). JPMorganChase at AWS re:Invent. https://aws.amazon.com/tw/events/aws-reinvent-2024-recap/jpmorganchase/，檢索日期：2025年6月28日。
44.Bajaj, S., & Aggarwal, N. (2021, May 25). Roostify simplifies mortgage processing with Lending DocAI. Google Cloud Blog. https://cloud.google.com/blog/products/ai-machine-learning/roostify-reduces-mortgage-processing-times-with-google-cloud
45.DBS. (2023). DBS’ AI-powered digital transformation https://www.dbs.com/artificial-intelligence-machine-learning/artificial-intelligence/dbs-ai-powered-digital-transformation.html
46.DBS. (2024). AI in banking: Transforming the way we work with AI. https://www.dbs.com/artificial-intelligence-machine-learning/artificial-intelligence/singapore-fintech-festival-ai-in-banking-finance.html
47.Deloitte Center for Financial Services. (2024). Generative AI is expected to magnify the risk of deepfakes and other fraud in banking.https://www.deloitte.com/us/en/insights/industry/financial-services/deepfake-banking-fraud-risk-on-the-rise.html
48.Fredrik Filipsson. (2025, February 14). AI Case Study: AI for Automated Document Processing at JPMorgan (COIN). Redress Compliance. https://redresscompliance.com/ai-case-study-ai-for-automated-document-processing-at-jpmorgan-coin/#Conclusion
49.IBM. (n.d.). JPMC improves model risk management and governance against fraud. https://mediacenter.ibm.com/media/JP+Morgan+ChaseA+Improved+risk+management+models+with+IBM+Watson+Studio/1_ip9ff5mr，檢索日期：2025年6月28日。
50.Insights, O. (2023). Government artificial intelligence readiness index 2023. https://oxfordinsights.com/wp-content/uploads/2023/12/2023-Government-AI-Readiness-Index-1.pdf
51.Jimmy Ng. (2023, August 7). DBS’ AI-powered digital transformation. DBS.com. https://www.dbs.com/artificial-intelligence-machine-learning/artificial-intelligence/dbs-ai-powered-digital-transformation.html
52.JP Morgan Chase & Co. (2017, Feb 16). Chase to Launch Customer-Driven Digital Mortgage Experience. https://media.chase.com/news/chase-to-launch-customer-driven-digital-mortgage-experience
53.Morgan Stanley. (2024, June 26). Morgan Stanley Wealth Management Announces Latest Game-Changing Addition to Suite of GenAI Tools. https://www.morganstanley.com/press-releases/ai-at-morgan-stanley-debrief-launch
54.NIST. (n.d.). AI Risk Management Framework. https://www.nist.gov/itl/ai-risk-management-framework
55.OpenAI. (n.d.). Introducing ChatGPT. https://openai.com/index/chatgpt/，檢索日期：2025年6月28日。
56.PwC. (2024). Only 2% of businesses have implemented firm-wide cyber resilience, even as cyber security concerns are top-of-mind and the average data breach exceeds US$3M: PwC 2025 Global Digital Trust Insights. https://www.pwc.com/gx/en/news-room/press-releases/2024/pwc-2025-global-digital-trust-insights.html
57.PwC. (n.d.). PwC’s 2024 US Responsible AI Survey. https://www.pwc.com/us/en/tech-effect/ai-analytics/responsible-ai-survey.html
58.Vanguri, S. (2022, October 20). Lending DocAI fast tracks the home loan process. Google Cloud. https://cloud.google.com/blog/products/ai-machine-learning/lending-docai-fast-tracks-the-home-loan-process | zh_TW |
