[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"article-ai-big-data-blockchain-finance-convergence-zh":3,"tags-ai-big-data-blockchain-finance-convergence-zh":37,"related-lang-ai-big-data-blockchain-finance-convergence-zh":45,"related-posts-ai-big-data-blockchain-finance-convergence-zh":49,"series-research-53a6508d-4883-4475-a754-31ac7b262c76":86},{"id":4,"title":5,"content":6,"summary":7,"source":8,"source_url":9,"author":10,"image_url":11,"keywords":12,"language":21,"translated_content":10,"views":22,"is_premium":23,"created_at":24,"updated_at":24,"cover_image":11,"published_at":25,"rewrite_status":26,"rewrite_error":10,"rewritten_from_id":27,"slug":28,"category":29,"related_article_id":30,"status":31,"google_indexed_at":32,"x_posted_at":10,"tweet_text":10,"title_rewritten_at":10,"title_original":10,"key_takeaways":33,"topic_cluster_id":10,"embedding":10,"is_canonical_seed":23},"53a6508d-4883-4475-a754-31ac7b262c76","AI、大數據、區塊鏈怎麼接上金融","\u003Cp data-speakable=\"summary\">這篇在講 \u003Ca href=\"\u002Fnews\u002Fcrypto-built-for-ai-agents-not-humans-zh\">AI\u003C\u002Fa>、大數據、\u003Ca href=\"\u002Fnews\u002Fblockchain-governance-legal-problem-zh\">區塊鏈\u003C\u002Fa>怎麼一起用在金融，重點是整合架構和風險控制。\u003C\u002Fp>\u003Cp>Springer 在 \u003Ca href=\"https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-981-92-0126-6_30\" target=\"_blank\" rel=\"noopener\">SpringerLink\u003C\u002Fa> 收錄了這篇章節。它不是在賣產品。它是在講一個金融科技架構怎麼拼起來。\u003C\u002Fp>\u003Cp>作者 Mohamed Amine Issami 把三件事放在一起看。\u003Ca href=\"https:\u002F\u002Flink.springer.com\u002Fsearch?query=artificial+intelligence\" target=\"_blank\" rel=\"noopener\">人工智慧\u003C\u002Fa>、\u003Ca href=\"https:\u002F\u002Flink.springer.com\u002Fsearch?query=big+data\" target=\"_blank\" rel=\"noopener\">大數據\u003C\u002Fa>、\u003Ca href=\"https:\u002F\u002Flink.springer.com\u002Fsearch?query=blockchain\" target=\"_blank\" rel=\"noopener\">區塊鏈\u003C\u002Fa>。講白了，就是資料餵 AI，AI 做判斷，區塊鏈負責留痕。\u003C\u002Fp>\u003Cp>這篇出自 \u003Ca href=\"https:\u002F\u002Flink.springer.com\u002Fbook\u002F10.1007\u002F978-981-92-0126-6\" target=\"_blank\" rel=\"noopener\">Financial Technology\u003C\u002Fa>，屬於 \u003Ca href=\"https:\u002F\u002Flink.springer.com\u002Fseries\u002F7899\" target=\"_blank\" rel=\"noopener\">Communications in Computer and Information Science\u003C\u002Fa> 系列。也收在 \u003Ca href=\"https:\u002F\u002Flink.springer.com\u002Fconference\u002Ficft\" target=\"_blank\" rel=\"noopener\">ICFT 2025\u003C\u002Fa>。所以它比較像研究筆記，不是行銷稿。\u003C\u002Fp>\u003Ctable>\u003Cthead>\u003Ctr>\u003Cth>項目\u003C\u002Fth>\u003Cth>數值\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Ctd>章節標題\u003C\u002Ftd>\u003Ctd>Artificial Intelligence, Big Data, and Blockchain: The Synergistic Convergence Reshaping Financial Services\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>出版日期\u003C\u002Ftd>\u003Ctd>2026-04-29\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>頁碼\u003C\u002Ftd>\u003Ctd>370–380\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Print ISBN\u003C\u002Ftd>\u003Ctd>978-981-92-0125-9\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Online ISBN\u003C\u002Ftd>\u003Ctd>978-981-92-0126-6\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>DOI\u003C\u002Ftd>\u003Ctd>10.1007\u002F978-981-92-0126-6_30\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003Ch2>這篇到底在說什麼\u003C\u002Fh2>\u003Cp>核心論點很直白。三種技術合在一起，比單獨用更有用。大數據提供資料，AI 做預測和自動化，\u003Ca href=\"\u002Ftag\u002F區塊鏈\">區塊鏈\u003C\u002Fa>提供可追溯的紀錄層。\u003C\u002Fp>\n\u003Cfigure class=\"my-6\">\u003Cimg src=\"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1777922468990-w2sz.png\" alt=\"AI、大數據、區塊鏈怎麼接上金融\" class=\"rounded-xl w-full\" loading=\"lazy\" \u002F>\u003C\u002Ffigure>\n\u003Cp>作者把這套東西叫做 CMFT。全名是 convergence model for fintech technologies。名字很學術，但意思不難懂。就是把金融科技當成一個系統，不是很多孤島。\u003C\u002Fp>\u003Cp>我覺得這個切法很合理。金融業最怕的，不是單點失誤。是資料、模型、稽核紀錄三邊斷線。詐欺偵測、放款審核、跨境結算，都很吃這種整體設計。\u003C\u002Fp>\u003Cul>\u003Cli>AI 需要更乾淨的資料管線。\u003C\u002Fli>\u003Cli>區塊鏈能記錄誰改了什麼。\u003C\u002Fli>\u003Cli>大數據放大模型能力，也放大壞資料風險。\u003C\u002Fli>\u003Cli>金融系統要一起通過法遵檢查。\u003C\u002Fli>\u003C\u002Ful>\u003Ch2>風險才是這篇最好看的地方\u003C\u002Fh2>\u003Cp>這篇沒有把整合講成神話。它直接點出三個問題：AI 模型污染、區塊鏈擴展性、系統性脆弱點。這三個字眼很硬，但很實際。\u003C\u002Fp>\u003Cp>原因很簡單。模型被餵壞資料，決策就會歪。鏈上交易太多，驗證就會塞車。整合層如果寫不好，小 bug 會變成大事故。金融業最怕這種連鎖反應。\u003C\u002Fp>\u003Cp>這句話很貼切。Mohamed Amine Issami 說：\u003C\u002Fp>\u003Cblockquote>“The future of finance is not about silos, but about integrated systems that can be trusted, transparent, and secure.” — Mohamed Amine Issami\u003C\u002Fblockquote>\u003Cp>這句話的重點不是口號。重點是信任要靠設計，不是靠宣傳。AI 的判斷和區塊鏈的紀錄，如果沒有治理，照樣會出包。\u003C\u002Fp>\u003Ch2>和現在的金融科技堆疊比起來\u003C\u002Fh2>\u003Cp>現在很多團隊是分開買工具。AI 做分類，區塊鏈做記錄，大數據平台做儲存。這樣能跑，但很像把不同品牌零件硬湊在一起。\u003C\u002Fp>\n\u003Cfigure class=\"my-6\">\u003Cimg src=\"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1777922452530-aovw.png\" alt=\"AI、大數據、區塊鏈怎麼接上金融\" class=\"rounded-xl w-full\" loading=\"lazy\" \u002F>\u003C\u002Ffigure>\n\u003Cp>這篇的意思是，下一步要把它們當成同一個操作系統。這不是一句漂亮話。它牽涉到延遲、合規、資料血緣、權限控管，還有稽核流程。\u003C\u002Fp>\u003Cp>差異可以很直接地看：\u003C\u002Fp>\u003Cul>\u003Cli>單獨 AI 很快，但來源不一定可信。\u003C\u002Fli>\u003Cli>單獨區塊鏈有紀錄，但速度常常拖。\u003C\u002Fli>\u003Cli>單獨大數據能吃很多資料，但不保證判斷更準。\u003C\u002Fli>\u003Cli>整合架構可以做個人化金融服務，但前提是治理先到位。\u003C\u002Fli>\u003C\u002Ful>\u003Cp>文中也提到 dec\u003Ca href=\"\u002Fnews\u002Fwhy-ai-agent-registries-are-the-new-attack-surface-zh\">entr\u003C\u002Fa>alized AI 和 zero-knowledge proofs。這兩個名詞不是拿來裝酷。前者可以分散訓練或推論，後者可以證明某件事成立，卻不必把原始資料全攤開。\u003C\u002Fp>\u003Cp>這對金融很有吸引力。因為隱私和稽核常常互相拉扯。你想保密，又想讓監管看得懂。這時候 ZKP 就很有戲。\u003C\u002Fp>\u003Ch2>為什麼治理不能放最後\u003C\u002Fh2>\u003Cp>這篇最成熟的地方，是把治理放進架構裡。很多文章都把法規當成事後補丁。這篇不是。它直接把 governance、privacy、oracle design 放進同一張圖。\u003C\u002Fp>\u003Cp>這很重要。因為 AI 不是只在模型層出問題。資料來源也會出問題。區塊鏈不是只在共識層出問題。應用層和 oracle 也會出問題。\u003C\u002Fp>\u003Cp>作者最後還提到量子時代的威脅。這不是在嚇人。因為只要金融系統越來越依賴密碼學，量子風險就不再只是實驗室新聞。\u003C\u002Fp>\u003Cp>如果你是銀行、監管單位、或 fintech 團隊，這篇其實在講一件事：不要把 AI、資料、區塊鏈分開評估。要一起測。\u003C\u002Fp>\u003Cp>你要一起看模型完整性、資料來源、延遲、法遵成本、隱私控制。分開看，會漏掉很多系統問題。\u003C\u002Fp>\u003Ch2>這跟產業現況有什麼關係\u003C\u002Fh2>\u003Cp>金融業現在很愛講 AI。也很愛講 tokenization。還有上鏈、RWA、智慧合約。可是很多專案卡住，不是因為技術不夠炫，而是因為資料管線和治理沒接好。\u003C\u002Fp>\u003Cp>這篇論文提醒了一件很現實的事。技術整合的成本，往往比單點技術高。你不只要會寫模型，還要會處理資料品質、權限、稽核、容災，還有跨部門協作。\u003C\u002Fp>\u003Cp>所以真正的問題不是「要不要用 AI 或區塊鏈」。問題是「哪一種組合，能撐過壓力測試」。這才是金融業會在意的地方。\u003C\u002Fp>\u003Ch2>我會怎麼看這篇\u003C\u002Fh2>\u003Cp>我覺得這篇最大的價值，是把三種常被分開講的技術，拉回同一個金融場景。它沒有亂吹，也沒有把每個名詞都包裝成解方。\u003C\u002Fp>\u003Cp>如果要我下結論，我會說：AI 負責判斷，大數據負責養資料，區塊鏈負責留證據。三者一起上，才有機會做出可查、可控、可維運的金融系統。\u003C\u002Fp>\u003Cp>下一步值得看的，不是誰先喊口號。是誰先把資料血緣、模型治理、鏈上紀錄串起來，還能通過法遵和壓力測試。這種團隊，才真的有機會活下來。\u003C\u002Fp>","Springer 的 ICFT 2025 論文整理 AI、大數據、區塊鏈在金融的整合方式，也點出模型污染、擴展性與治理風險。","link.springer.com","https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-981-92-0126-6_30",null,"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1777922468990-w2sz.png",[13,14,15,16,17,18,19,20],"AI","大數據","區塊鏈","金融科技","Springer","ICFT 2025","治理","資料血緣","zh",4,false,"2026-05-04T19:20:37.123573+00:00","2026-05-04T19:20:37.099+00:00","done","5abf31ef-f4dc-44a4-80e9-e860a9320b41","ai-big-data-blockchain-finance-convergence-zh","research","8e325341-ee9d-4b99-bffc-9fd818221970","published","2026-05-05T09:00:18.453+00:00",[34,35,36],"AI、大數據、區塊鏈合在一起，重點是整合架構，不是單點工具。","這篇最實際的部分，是把模型污染、擴展性和治理風險一起看。","金融業如果要落地，得同時處理資料品質、稽核、隱私和延遲。",[38,39,41,42,44],{"name":14,"slug":14},{"name":17,"slug":40},"springer",{"name":15,"slug":15},{"name":13,"slug":43},"ai",{"name":16,"slug":16},{"id":30,"slug":46,"title":47,"language":48},"ai-big-data-blockchain-finance-convergence-en","How AI, Big Data, and Blockchain Fit Together in Finance","en",[50,56,62,68,74,80],{"id":51,"slug":52,"title":53,"cover_image":54,"image_url":54,"created_at":55,"category":29},"667b72b6-e821-4d68-80a1-e03340bc85f1","turboquant-seo-shift-small-sites-zh","TurboQuant 與小站 SEO 變化","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1778840440690-kcw9.png","2026-05-15T10:20:27.319472+00:00",{"id":57,"slug":58,"title":59,"cover_image":60,"image_url":60,"created_at":61,"category":29},"381fb6c6-6da7-4444-831f-8c5eed8d685c","turboquant-vllm-comparison-fp8-kv-cache-zh","TurboQuant 與 FP8 實測結果","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1778839867551-4v9g.png","2026-05-15T10:10:36.034569+00:00",{"id":63,"slug":64,"title":65,"cover_image":66,"image_url":66,"created_at":67,"category":29},"c15f45ee-a548-4dbf-8152-91de159c1a11","llmbda-calculus-agent-safety-rules-zh","LLMbda 演算替 AI 代理人立安全規則","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1778825503412-mlbf.png","2026-05-15T06:10:34.832664+00:00",{"id":69,"slug":70,"title":71,"cover_image":72,"image_url":72,"created_at":73,"category":29},"0c02225c-d6ff-44f8-bc92-884c8921c4a3","low-complexity-beamspace-denoiser-mmwave-mimo-zh","更簡單的毫米波波束域去噪器","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1778814650361-xtc2.png","2026-05-15T03:10:30.06639+00:00",{"id":75,"slug":76,"title":77,"cover_image":78,"image_url":78,"created_at":79,"category":29},"9d27f967-62cc-433f-8cdb-9300937ade13","ai-benchmark-wins-cyber-scare-defenders-zh","為什麼 AI 基準賽在資安領域的勝利，應該讓防守方警醒","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1778807450006-nofx.png","2026-05-15T01:10:29.379041+00:00",{"id":81,"slug":82,"title":83,"cover_image":84,"image_url":84,"created_at":85,"category":29},"bc402dc6-5da6-46fc-9d66-d09cb215f72b","why-linux-security-needs-patch-wave-mindset-zh","為什麼 Linux 安全需要「補丁浪潮」思維","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1778741449813-s2wn.png","2026-05-14T06:50:24.052583+00:00",[87,92,97,102,107,112,117,122,127,132],{"id":88,"slug":89,"title":90,"created_at":91},"f18dbadb-8c59-4723-84a4-6ad22746c77a","deepmind-bets-on-continuous-learning-ai-2026-zh","DeepMind 押注 2026 連續學習 AI","2026-03-26T08:16:02.367355+00:00",{"id":93,"slug":94,"title":95,"created_at":96},"f4a106cb-02a6-4508-8f39-9720a0a93cee","ml-papers-of-the-week-github-research-desk-zh","每週 ML 論文清單，為何紅到 GitHub","2026-03-27T01:11:39.284175+00:00",{"id":98,"slug":99,"title":100,"created_at":101},"c4f807ca-4e5f-47f1-a48c-961cf3fc44dc","ai-ml-conferences-to-watch-in-2026-zh","2026 AI 研討會投稿時程整理","2026-03-27T01:51:53.874432+00:00",{"id":103,"slug":104,"title":105,"created_at":106},"9f50561b-aebd-46ba-94a8-363198aa7091","openclaw-agents-manipulated-self-sabotage-zh","OpenClaw Agent 會自己搞砸自己","2026-03-28T03:03:18.786425+00:00",{"id":108,"slug":109,"title":110,"created_at":111},"11f22e92-7066-4978-a544-31f5f2156ec6","vega-learning-to-drive-with-natural-language-instructions-zh","Vega：使用自然語言指示進行自駕車控制","2026-03-28T14:54:04.847912+00:00",{"id":113,"slug":114,"title":115,"created_at":116},"a4c7cfec-8d0e-4fec-93cf-1b9699a530b8","drive-my-way-en-zh","Drive My Way：個性化自駕車風格的實現","2026-03-28T14:54:26.207495+00:00",{"id":118,"slug":119,"title":120,"created_at":121},"dec02f89-fd39-41ba-8e4d-11ede93a536d","training-knowledge-bases-with-writeback-rag-zh","用 WriteBack-RAG 強化知識庫提升檢索效能","2026-03-28T14:54:45.775606+00:00",{"id":123,"slug":124,"title":125,"created_at":126},"3886be5c-a137-40cc-b9e2-0bf18430c002","packforcing-efficient-long-video-generation-method-zh","PackForcing：短影片訓練也能生成長影片","2026-03-28T14:55:02.688141+00:00",{"id":128,"slug":129,"title":130,"created_at":131},"72b90667-d930-4cc9-8ced-aaa0f8968d44","pixelsmile-toward-fine-grained-facial-expression-editing-zh","PixelSmile：提升精細臉部表情編輯的新方法","2026-03-28T14:55:20.678181+00:00",{"id":133,"slug":134,"title":135,"created_at":136},"cf046742-efb2-4753-aef9-caed5da5e32e","adaptive-block-scaled-data-types-zh","IF4：神經網路量化的聰明選擇","2026-03-31T06:00:36.990273+00:00"]