請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/101307完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 邱宏仁 | zh_TW |
| dc.contributor.advisor | Hong-Jen Chiu | en |
| dc.contributor.author | 倪帝光 | zh_TW |
| dc.contributor.author | Diepak Nirmalsingh | en |
| dc.date.accessioned | 2026-01-14T16:08:09Z | - |
| dc.date.available | 2026-01-15 | - |
| dc.date.copyright | 2026-01-14 | - |
| dc.date.issued | 2025 | - |
| dc.date.submitted | 2025-12-31 | - |
| dc.identifier.citation | Accenture. (2025). Unlocking the value of generative AI in commercial payments. https://bankingblog.accenture.com/unlocking-gen-ai-commercial-payments
Arcade. (2025). Secure AI agents in wealth management. https://blog.arcade.dev/build-ai-agents-for-asset-wealth-management Bank for International Settlements. (2023). Considerations for the use of stablecoin arrangements in cross‑border payments (CPMI Report D220). https://www.bis.org/cpmi/publ/d220.htm Bank for International Settlements. (2024). Annual Economic Report 2024: The future monetary system. https://www.bis.org/publ/arpdf/ar2024e3.htm Binadox. (2025). LLM API pricing comparison 2025: Complete cost analysis guide. https://www.binadox.com/blog/llm-api-pricing-comparison-2025-complete-cost-analysis-guide/ BVNK. (2024). Europe’s MiCA regime is bringing new confidence to crypto payments. https://bvnk.com/blog/europes-mica-regime-crypto-payments De La Cruz, A. (2025). Multi‑agent large language models for traditional finance and DeFi. Journal of Intelligent Engineering and Applied Sciences. https://www.suaspress.org/ojs/index.php/JIEAS/article/view/v3n1a02/v3n1a02 European Parliament. (2025). Report on the impact of artificial intelligence on the financial sector. https://www.europarl.europa.eu/doceo/document/A-10-2025-0225_EN.html Financial Stability Board. (2025). G20 roadmap for enhancing cross‑border payments: 2025 progress report and KPI update. https://www.fsb.org/uploads/P091025-1.pdf Fujitsu. (2025). AI agents and the transformation of the financial industry. https://global.fujitsu/zh-tw/insight/tl-aiagents-financial-industry-20250418 International Monetary Fund. (2023). Institutional arrangements for fintech regulation (Fintech Note FTN/2023/004). https://www.imf.org/-/media/files/publications/ftn063/2023/english/ftnea2023004.pdf Intuition Labs. (2025). LLM API Pricing Comparison (2025): OpenAI, Gemini, and more. https://intuitionlabs.ai/articles/llm-api-pricing-comparison-2025 Laozhang. (2025). GPT‑4o pricing guide 2025: Complete cost analysis & calculator. https://blog.laozhang.ai/ai-tools/openai-gpt4o-pricing-guide/ McKinsey & Company. (2024). Global Payments Report 2024: Simpler Interfaces, Complex Reality. https://www.mckinsey.com/industries/financial-services/our-insights/global-payments-in-2024-simpler-interfaces-complex-reality MinterEllisonRuddWatts. (2025). AML/CFT: DIA releases guidance for VASPs. https://www.minterellison.co.nz/insights/aml-cft-dia-releases-guidance-for-vasps Nurix AI. (2025). What are AI agent workflows? Top use cases with autonomous agents. https://www.nurix.ai/blogs/ai-agent-workflows-industries Paluy, D. (2025). Enabling autonomous AI agents to make payments: Challenges and design patterns. https://majesticlabs.dev/blog/202504/enabling-autonomous-ai-agents-to-make-payments/ PricePerToken. (2025). Anthropic Claude‑3.5‑Haiku pricing (updated 2025). https://pricepertoken.com/pricing-page/model/anthropic-claude-3.5-haiku Statista. (2024). Software-as-a-Service (SaaS) Market Report 2024. https://www.statista.com/outlook/tmo/it-services/software-as-a-service/ TechCrunch. (2024). Anthropic hikes the price of its Haiku model. https://techcrunch.com/2024/11/04/anthropic-hikes-the-price-of-its-haiku-model/ TD Economics. (2025, September). Stablecoins Enter the Mainstream. https://economics.td.com/us-stablecoins-enter-the-mainstream Visa. (2024). Visa Completes USDC Settlement Pilot on Solana. https://usa.visa.com/about-visa/newsroom/press-releases.releaseId.21791.html World Bank. (2023). Remittance Prices Worldwide, Issue 46. https://remittanceprices.worldbank.org World Economic Forum. (2025). How agentic AI will transform financial services: Autonomy, efficiency and inclusion. https://www.weforum.org/stories/2024/12/agentic-ai-financial-services-autonomy-efficiency-and-inclusion/ World Economic Forum & Cambridge Centre for Alternative Finance. (2025). Artificial Intelligence in Financial Services. https://reports.weforum.org/docs/WEF_Artificial_Intelligence_in_Financial_Services_2025.pdf | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/101307 | - |
| dc.description.abstract | GenPay AI 是一個新一代的支付自動化平台,旨在協助金融機構、支付處理商以及金融科技公司,在不需更換其既有系統的情況下,現代化其全球支付系統。該平台解決了一個明確的產業問題:跨境交易至今仍然緩慢、成本高昂且高度碎片化。隨著穩定幣與區塊鏈支付網路的快速發展,多數機構在有效整合這些新興技術時仍面臨困難,進而導致更高的營運成本、不一致的支付路由,以及更大的法規風險。
GenPay AI 透過導入一個雲端化、以人工智慧驅動的智慧層來回應上述挑戰。此智慧層透過單一且統一的 API,將傳統支付系統與區塊鏈支付系統進行整合。平台核心由三個專門化的 AI 代理所構成:GenRoute,負責在法幣與區塊鏈網路之間尋找最快且最具成本效益的支付路徑;GenBridge,透過 Fireblocks 與 Circle 等合作夥伴執行交易;以及 GenShield,負責自動化執行 AML/KYC 驗證、制裁名單篩選與異常行為偵測等合規檢查。 這三個 AI 代理協同運作,可實現更快速的結算速度、更低的交易成本,以及更高程度的法規一致性。此外,GenPay AI 採用混合式 AI 模型策略,將日常與低複雜度任務交由成本效率較高的輕量模型(如 Claude Haiku 4.5)處理,而僅在需要高度推理能力的複雜決策情境中使用先進模型(如 GPT-5)。此策略在維持高準確度與可靠性的同時,可有效降低運算成本,最高可節省約 80%。 市場研究顯示,市場對此類解決方案具有高度需求。穩定幣交易量的快速成長、法規環境的逐漸明朗,以及機構投資者對數位支付的興趣提升,共同為 GenPay AI 創造了有利的發展環境。其軟體即服務(SaaS)商業模式結合訂閱制與交易導向的收費方式,使平台能隨著客戶交易量成長而具備高度擴展性與營運槓桿。 財務預測顯示,一支精簡的台灣工程團隊可於 6 至 8 個月內完成最小可行產品(MVP)的開發,且每月營運成本預估約為 23,000 至 29,000 美元。在保守的採用情境下,從初期 3 家客戶到擴展至 25 家客戶,GenPay AI 皆展現出隨交易量成長而具備顯著獲利潛力的可能性。 整體而言,GenPay AI 提供了一個結合人工智慧、區塊鏈與支付自動化的實用、可擴展且財務上具可行性的解決方案。平台定位為銀行與金融科技機構的策略性基礎建設合作夥伴,同時兼顧創新能力與合規確定性,為未來的商業化發展、投資人參與以及長期成長奠定穩固基礎。 | zh_TW |
| dc.description.abstract | GenPay AI is a next-generation payment automation platform that helps financial institutions, payment processors, and fintech companies modernize their global payment systems without replacing the current systems they already use. It solves a clear industry problem: cross-border transactions are still slow, expensive, and fragmented. As stablecoins and blockchain-based networks grow, most institutions struggle to integrate them efficiently. This leads to higher costs, inconsistent routing, and greater regulatory risk.
GenPay AI addresses these issues by introducing a cloud-based, AI-powered intelligence layer that connects traditional and blockchain payment systems through a single, unified API. The platform is built around three specialized AI agents; GenRoute, which finds the fastest and most cost-effective route across fiat and blockchain networks; GenBridge, which executes transactions through partners such as Fireblocks and Circle; and GenShield, which automates compliance checks like AML/KYC verification, sanctions screening, and anomaly detection. Together, these agents deliver faster settlements, lower costs, and stronger regulatory alignment. GenPay AI also uses a hybrid AI model strategy where it deploys lighter, cost-efficient models like Claude Haiku 4.5 for routine tasks, and advanced models like GPT-5 only for complex decision-making. This approach reduces computing expenses by up to 80% while keeping accuracy and reliability high. Market research shows strong demand for this type of solution. The fast growth of stablecoin transactions, clearer regulations, and growing institutional interest in digital payments all create a favorable environment for GenPay AI. Its SaaS business model combines subscription fees with transaction-based pricing, which allows for scalability and high operating leverage as client activity increases. Financial projections show that a lean engineering team in Taiwan can deliver the minimum viable product (MVP) within 6–8 months, with monthly operating costs estimated at USD 23,000–29,000. Based on conservative adoption scenarios, from a small base of 3 clients to a larger rollout of 25, GenPay AI shows meaningful profitability potential as transaction volume increases. Overall, GenPay AI offers a practical, scalable, and financially sound solution that combines AI, blockchain, and payment automation. It positions itself as a strategic infrastructure partner for banks and fintechs who are looking for both innovation and compliance certainty. This creates a solid foundation for future commercialization, investor engagement, and long-term growth. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2026-01-14T16:08:09Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2026-01-14T16:08:09Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | Acknowledgment ii
執行摘要 iii Executive Summary v Table of Contents vii List of Tables xi List of Figures xii List of Abbreviations xiii 1. Business Description 1 1.1 Company Overview 1 1.2 Value Proposition 2 1.3 Competitive Advantages 3 1.4 Business Model 4 1.5 Mission Statement 4 1.6 Vision Statement 4 1.7 ESG Impact 5 2. Product Description 6 2.1 Overview 6 2.2 Core Products 7 2.3 Product Features 8 2.4 Product Differentiation 9 2.5 Service Offerings 10 2.6 Pricing Model 11 2.7 Use Cases 12 3. Integration Model 13 3.1 Overview 13 3.2 Leveraging Established AI Frameworks 13 3.3 Integrating with Fintech Infrastructure 15 3.3.1 Integration with Fireblocks 15 3.3.2 Integration with Circle 16 3.4 Strategic Positioning of The Intelligence Layer 17 3.5 Solving the Industry Gap 18 4. Technical Design of AI Agents 19 4.1 Overview of the Agent System 19 4.1.1 GenRoute – AI Decision Engine 20 4.1.2 GenBridge – Blockchain Settlement Gateway 21 4.1.3 GenShield – Compliance & Risk Layer 22 4.2 Coordination and Data Flow 23 4.2.1 Transaction Flow 23 4.2.2 Hybrid AI Routing Within the Agent System 24 4.3 Data Inputs and Decision Logic 25 4.3.1 AI Agents Selection Logic 25 4.3.2 Hybrid AI Model Selection Logic 26 4.4 Infrastructure and Scalability 27 4.5 Advantages of the Agent-Based Architecture 28 5. Market Research 29 5.1 Industry Overview 29 5.2 Current Industry Problems 30 5.3 Trends and Opportunity 31 5.4 Competitive Landscape 32 5.5 Competitive Gap Analysis 34 5.5.1 User Acceptance 34 5.5.2 Cost Advantage 35 5.5.3 Regulatory Feasibility 35 5.5.4 Social Legitimacy 36 6. Financial Feasibility 38 6.1 Cost Structure Overview 38 6.2 Initial Capital Requirements (Month 0–6) 41 6.3 Monthly OPEX (Month 7+) 42 6.4 Revenue Projection – Potential Scenarios 43 6.5 Burn Rate Analysis – Projected Financial Timeline (18-Month) 47 6.6 Cash Runway Scenarios 48 7. Risk and Uncertainty 49 7.1 Market and Adoption Risks 49 7.2 Regulatory and Compliance Risks 50 7.3 Technical and Operational Risks 51 7.4 Financial Risks 52 References 53 Appendix 1: System & Product Architecture 56 Appendix 2: Data Flow 58 Appendix 3: Hybrid AI Model 59 | - |
| dc.language.iso | en | - |
| dc.subject | 人工智慧 | - |
| dc.subject | 區塊鏈支付 | - |
| dc.subject | 穩定幣 | - |
| dc.subject | 支付自動化 | - |
| dc.subject | 金融科技 | - |
| dc.subject | Artificial Intelligence | - |
| dc.subject | Blockchain Payments | - |
| dc.subject | Stablecoins | - |
| dc.subject | Payment Automation | - |
| dc.subject | FinTech | - |
| dc.title | GenPay AI:一項以人工智慧驅動之區塊鏈支付基礎建設的商業計畫 | zh_TW |
| dc.title | GenPay AI: A Business Plan for an AI-Driven Blockchain Payment Infrastructure | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 114-1 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 林嘉薇 ;陳思帆 | zh_TW |
| dc.contributor.oralexamcommittee | Joy Lin;Szu-fan Chen | en |
| dc.subject.keyword | 人工智慧,區塊鏈支付穩定幣支付自動化金融科技 | zh_TW |
| dc.subject.keyword | Artificial Intelligence,Blockchain PaymentsStablecoinsPayment AutomationFinTech | en |
| dc.relation.page | 59 | - |
| dc.identifier.doi | 10.6342/NTU202504867 | - |
| dc.rights.note | 同意授權(限校園內公開) | - |
| dc.date.accepted | 2025-12-31 | - |
| dc.contributor.author-college | 管理學院 | - |
| dc.contributor.author-dept | 企業管理碩士專班 | - |
| dc.date.embargo-lift | 2026-01-15 | - |
| 顯示於系所單位: | 管理學院企業管理專班(Global MBA) | |
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