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https://nccur.lib.nccu.edu.tw/handle/140.119/159176
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| Title: | 生成式人工智慧代理人技術在金融業應用之實證研究
An Empirical Study of Generative AI Agent Application in Financial Industry |
| Authors: | 林孜頤 |
| Contributors: | 尚孝純
林孜頤 |
| Keywords: | 金融業
人工智慧
AI代理人
代理人
生成式AI
Finance
AI Agent
Agentic AI
Generative AI |
| Date: | 2025 |
| Issue Date: | 2025-09-01 15:30:13 (UTC+8) |
| Abstract: | 本研究旨在探討生成式代理人人工智慧在金融產業中的應用情境、面臨挑戰與導入策略，並透過文獻回顧與多個實務個案進行比較分析。研究發現，目前全球金融業已開始在投資分析、風險管理、營運效率與客戶服務等面向應用生成式代理人人工智慧，惟在詐欺偵測方面，尚未觀察到具高代理程度（high agentic degree）的應用案例。本研究蒐集45個實際應用案例，最後選擇七家金融機構之應用場景進行深入探討。資料蒐集結果顯示，具高代理特徵的服務仍屬少數，目前多數應用仍集中於單一功能導向的生成式人工智慧服務。在導入生成式代理人人工智慧的過程中，金融機構主要面臨三大挑戰。第一，系統相關問題，如資料品質、資料安全與系統整合等。第二，合規限制，由於金融業高度受監管，生成式人工智慧須符合可信賴性、透明性與可解釋性等要求。第三，如何兼顧監管標準與模型效能，建立具信賴基礎的應用架構。在導入策略方面，金融機構通常從三個面向規劃：選擇適當合作夥伴、採取小規模試行策略（pilot to scale），以及提升內部人員的接受度與參與度。合作夥伴選擇方式包括與新創合作、與領先科技廠商合作和與金融相關業務服務合作。小規模導入有助於企業及早修正方向並評估應用成效；面對人才缺口，則可透過教育訓練與競賽式宣導活動，提升員工對生成式人工智慧工具的理解與接受度。
This study explores the application scenarios, challenges and limitations, and implementation strategies of generative agent-based artificial intelligence (AI) in the financial industry through a combination of literature review and practical case analysis. The findings indicate that while financial institutions around the world have begun adopting generative agent-based AI in areas such as investment analysis, risk management, operational efficiency, and customer support, there is still a notable absence of high-agentic-degree applications in fraud detection. Out of 45 real-world application cases reviewed, this study ultimately selected 7 cases for detailed analysis. Despite the above 7 cases, high-agentic AI applications remain rare. Most current deployments are centered around single-function generative AI services rather than fully autonomous agent-based solutions. Three major categories of challenges were identified in the implementation process. First, system-related issues such as data quality, data security, and integration complexity. Second, regulatory constraints, as financial services are subject to strict oversight and AI applications must meet standards of trustworthiness, transparency, and explainability. Third, striking a balance between meeting regulatory standards and ensuring model performance remains a core challenge in building trustworthy AI systems. In terms of implementation strategies, financial institutions typically focus on three key areas: selecting appropriate partners, adopting a pilot-to-scale approach, and increasing internal employee engagement. Partner selection strategies include collaboration with established technology firms, partnerships with startups and partnership with related business. Small-scale implementation helps institutions make timely adjustments and evaluate impact. To address talent gaps and foster organizational acceptance, institutions often conduct training programs and awareness campaigns such as internal competitions to enhance employees’ understanding and readiness for AI integration. |
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| Description: | 碩士
國立政治大學
國際金融碩士學位學程
112zb1030 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0112ZB1030 |
| Data Type: | thesis |
| Appears in Collections: | [國際金融碩士學位學程] 學位論文
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