[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"article-ai-big-data-blockchain-finance-convergence-zh":3,"article-related-ai-big-data-blockchain-finance-convergence-zh":32,"series-research-53a6508d-4883-4475-a754-31ac7b262c76":82},{"id":4,"slug":5,"title":6,"content":7,"summary":8,"source":9,"source_url":10,"author":11,"image_url":12,"cover_image":12,"category":13,"language":14,"translated_content":11,"related_article_id":15,"keywords":16,"key_takeaways":25,"views":29,"created_at":30,"published_at":31,"topic_cluster_id":11},"53a6508d-4883-4475-a754-31ac7b262c76","ai-big-data-blockchain-finance-convergence-zh","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","research","zh","8e325341-ee9d-4b99-bffc-9fd818221970",[17,18,19,20,21,22,23,24],"AI","大數據","區塊鏈","金融科技","Springer","ICFT 2025","治理","資料血緣",[26,27,28],"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。",5,"2026-05-04T19:20:37.123573+00:00","2026-05-04T19:20:37.099+00:00",{"tags":33,"relatedLang":41,"relatedPosts":45},[34,35,37,38,40],{"name":18,"slug":18},{"name":21,"slug":36},"springer",{"name":19,"slug":19},{"name":17,"slug":39},"ai",{"name":20,"slug":20},{"id":15,"slug":42,"title":43,"language":44},"ai-big-data-blockchain-finance-convergence-en","How AI, Big Data, and Blockchain Fit Together in Finance","en",[46,52,58,64,70,76],{"id":47,"slug":48,"title":49,"cover_image":50,"image_url":50,"created_at":51,"category":13},"a4cf24e5-b958-4f91-bdca-2f1a57e81aef","why-benchmark-leaderboards-are-wrong-about-model-logic-zh","為什麼基準排行榜看錯了模型邏輯","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780673571153-x7yi.png","2026-06-05T15:32:23.043639+00:00",{"id":53,"slug":54,"title":55,"cover_image":56,"image_url":56,"created_at":57,"category":13},"4a829d2a-24a3-42dd-8be4-49e5ab35435a","why-prompt-engineering-is-wrong-about-2026-zh","為什麼 2026 年 prompt engineering 錯了","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780661884287-ow45.png","2026-06-05T12:17:19.813402+00:00",{"id":59,"slug":60,"title":61,"cover_image":62,"image_url":62,"created_at":63,"category":13},"52a37532-880d-4261-8f62-2f254d6c592d","spire-evidence-grounded-ai-humanities-zh","SPIRE 讓人文 AI 更重證據","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780647483844-bcuj.png","2026-06-05T08:17:29.603104+00:00",{"id":65,"slug":66,"title":67,"cover_image":68,"image_url":68,"created_at":69,"category":13},"b38c56a6-e7f3-45fb-b100-d37e7b3ed417","reinforcement-aware-distillation-llm-reasoning-zh","強化感知蒸餾，想把推理一起學進去","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780646589500-0me6.png","2026-06-05T08:02:33.908932+00:00",{"id":71,"slug":72,"title":73,"cover_image":74,"image_url":74,"created_at":75,"category":13},"60f7d702-20a7-4cec-9a80-185f072c8dfe","next-token-models-plan-ahead-zh","次詞模型其實會先想一步","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780645684780-roea.png","2026-06-05T07:47:34.35089+00:00",{"id":77,"slug":78,"title":79,"cover_image":80,"image_url":80,"created_at":81,"category":13},"7ec803f7-2658-4c9e-baa6-2b8528407d7f","google-deepmind-co-scientist-researchers-zh","Google DeepMind 對外開放 Co-Scientist","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780636679231-q694.png","2026-06-05T05:17:30.68789+00:00",[83,88,93,98,103,108,113,118,123,128],{"id":84,"slug":85,"title":86,"created_at":87},"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":89,"slug":90,"title":91,"created_at":92},"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":94,"slug":95,"title":96,"created_at":97},"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":99,"slug":100,"title":101,"created_at":102},"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",{"id":104,"slug":105,"title":106,"created_at":107},"53a0dc54-0371-4e40-8d5e-74e94a73840c","geometry-aware-similarity-metrics-for-neural-representations-zh","超越距離測量：用微分幾何重新理解神經網路","2026-03-31T06:01:01.241968+00:00",{"id":109,"slug":110,"title":111,"created_at":112},"fee7d472-a775-4b1d-bbc2-1e8bca1bbf8b","on-the-fly-repulsion-in-the-contextual-space-for-rich-divers-zh","讓AI繪圖更有創意：用排斥力提升生成多樣性","2026-03-31T06:01:25.439673+00:00",{"id":114,"slug":115,"title":116,"created_at":117},"a9901203-d69b-447b-8854-15d14eab32b4","vision-aided-beam-prediction-cnn-eca-zh","影像輔助波束預測升級 CNN","2026-04-01T10:00:25.8073+00:00",{"id":119,"slug":120,"title":121,"created_at":122},"b55e7dd4-0a24-4b3d-804d-b0309a03f498","triple-band-fss-mimo-antenna-sub-6-ghz-zh","三頻 FSS MIMO 天線瞄準 sub-6 GHz","2026-04-01T13:18:36.857305+00:00",{"id":124,"slug":125,"title":126,"created_at":127},"f68290bd-e7f3-4b30-ba22-dcd4e0130a66","openclaw-1299-repos-eight-weeks-analysis-zh","OpenClaw 1299 個 Repo 的資料解讀","2026-04-02T05:03:45.208411+00:00",{"id":129,"slug":130,"title":131,"created_at":132},"ed9f80eb-eb02-4d35-8ad4-0ddf428751dd","beam-coherence-aware-combining-mmwave-mimo-zh","毫米波 MIMO 的雙階合併法","2026-04-02T05:27:26.897188+00:00"]