每家大型銀行都聲稱正在部署人工智慧。他們宣佈用於客服的聊天機器人、詐欺偵測系統與演算法交易台。但這些多半只是將逐步自動化疊加在數十年舊基礎設施之上，並非根本性革新。
金融產業在 2025 年面臨的關鍵問題，不是銀行會不會使用 AI，而是 AI 是否會徹底重構銀行本身——將金融機構轉變為真正的智慧系統，每一個流程、決策與客戶互動都交由人工智慧主導。
摩根大通（JPMorgan Chase，全球市值最大的銀行）推動所謂「全面 AI 連結企業」，讓全體員工採用 AI 代理，從後台流程到客戶體驗全面自動化。這個願景遠超現今大多數銀行科技專案僅做表層自動化的現況，是從根本上重新想像銀行本質與運作方式。要理解這場轉型，必須分辨行銷話術與系統性變革，考察新興技術能力和對組織、經濟、監管帶來的深遠影響。
這場變局利害關係重大。麥肯錫顧問公司估算，生成式 AI 若在監管合規、客服、軟體開發、風險管理領域得到充分應用，未來銀行業每年可新增 2,000 億到 3,400 億美元產值。但要釋放這潛力不僅只是部署新工具，更需從根本重建銀行：突破陳舊遺留系統、應對未知監管環境、並處理勞動力變動，這可能徹底改寫產業就業樣貌。
本文探討如何打造一個真正 AI 驅動的銀行。以摩根大通的領先實踐為案例，剖析 AI 如何重構核心銀行業務，解釋代理式 AI 能自主決策的技術進展，探討對勞動力的影響，評估競爭環境，直面落地挑戰，審視監管與倫理疑慮，比較傳統銀行 AI 和去中心化金融方案，並最終勾勒「純 AI 銀行」於成熟階段的樣貌。最終浮現的是徹底顛覆現狀的圖像——金融機構的本質將被根本重塑，人類組織與智慧系統的界線也將變得模糊。
定義 AI 銀行：超越表層自動化
「AI 銀行」這詞彙已因過度濫用而失去意義。每家金融機構都用某種機器學習做信用評分、防詐騙、客戶分群。這些確實是科技進展，但並非徹底轉型。要理解真正的 AI 銀行與傳統加裝 AI 工具的銀行有何本質差異，必須審視幾個決定性特徵。
第一，AI 銀行在全營運層整合人工智慧，不只是個別環節導入。傳統銀行將 AI 部署於某些孤立區塊：這裡防詐系統、那邊聊天機器人，或特定市場演算法交易，這些系統之間少有協作或學習共享。真正的 AI 銀行則建立統一的智慧層，把所有系統、資料庫、流程整合起來。摩根大通的 LLM Suite 就是此路線的範例，每八週根據龐大內部數據持續訓練、更新 OpenAI 及 Anthropic 的大型語言模型，提供主要子公司應用。
第二，AI 銀行採用具備執行複雜多步任務且最低人工介入的代理式系統。這是一種質的飛躍。早期銀行科技只自動化特定、狹義的程序——像是過帳、產生例行報表、根據預設規則標記可疑行為。代理式 AI 則可以推理模糊情境、根據脈絡做決策，和整合過去需要人工判斷的工作流程。摩根大通已開始讓員工部署能處理多步驟任務的代理式 AI，這些代理人將在機構中實力日益強大，承擔更多責任。
第三，真正的 AI 銀行將工作徹底重組，圍繞 AI 能力而非現有職責。這意味着要重新定義工作任務、匯報結構和營運流程，不再是以新科技套到舊組織框架。這種區別甚為重要。若只是讓交易員使用 AI 分析工具，其實只是增強傳統職能；若是導入能自主操作且由人員監督的 AI 交易代理，則徹底改造了交易這項工作的本質。
第四，AI 銀行會推動持續學習系統，藉由實際營運資料不斷進化。不像傳統軟體那樣一成不變，AI 系統會從每一次客戶互動、交易模式、市場動態、以及運作結果積累經驗，愈用愈聰明，形成複利優勢。
最後，AI 銀行敢於實現端到端流程自動化，將過去需多個決策點人工介入的業務全部交給 AI。這不代表完全取代人力，而是根本改變人員在流程中的角色：人類成為指揮機器智慧的指揮家，而非純粹執行任務的工作者。
這些特徵區分了根本性轉型與漸進式改良。今日多數銀行都在傳統與純 AI 銀行之間的光譜上前進。摩根大通的現行專案是推動至後者端點最雄心勃勃的嘗試之一。
摩根大通：AI 優先銀行的藍圖
摩根大通的 AI 轉型，是目前全球傳統金融機構以人工智慧重建自身的最佳案例。核心是 LLM Suite——一套專屬平台，確保銀行營運必需的安全和合規前提下，讓員工得以應用先進語言模型。
銀行向 14 萬名員工推廣了 LLM Suite，成為業界最大規模的生成式 AI 企業落地之一。該平台於 2024 年夏問世，在 8 個月內用戶數突破 20 萬，部分動能來自員工自發性導入。這顯示該技術真正解決了工作需求，而非單純來自高層強制。
技術架構體現摩根大通兼顧創新與機構需求的思維。銀行沒自己從零開發基礎模型（即使以 180 億美元年科技預算也屬高難度），而是採多供應商語言模型 API 的入口平台，初期以 OpenAI 語言模型為主，日後按不同應用接入多模型—既不綁單一供應商，也可追蹤最新 AI 進展。
資料安全與智慧財產保護貫穿設計。摩根大通限制員工使用 ChatGPT，即為避免資料外洩，因此 LLM Suite 的模型運用與數據分離，資料供給不會反向訓練模型。這解決了金融機構的核心痛點：AI 要運作需巨量資料，而銀行資訊（客戶、交易、商業策略）極為敏感。入口平台方案，兼顧了 AI 能力與保密控制。
平台能力橫跨全行各大事業體。在投資銀行領域，AI 可大幅加速過往需投入無數分析員工時的資料整理。分析長 Derek Waldron 展示了 LLM Suite 用約 30 秒時間，為科技公司 CEO 產出 5 頁簡報的範例——傳統要好幾組員工熬夜趕工。銀行也訓練 AI 撰寫併購案專屬文件，這類文件動輒上百頁，需整合複雜的財務、法律與策略分析。
在消費金融業務上，應用聚焦提升營運效率與客戶服務。銀行推出了 EVEE Intelligent Q&A，一套生成式 AI 工具，讓客服專員可查詢並獲得簡明的 Chase 制度與文件說明，有效提升效率、縮短來電解決時間，增進員工的服務能力。 and customer satisfaction. This addresses a persistent challenge in consumer banking: customer service representatives must navigate vast repositories of product information, regulatory requirements, and procedural guidelines. AI that can instantly surface relevant information transforms their effectiveness.
和客戶滿意度。這解決了消費性銀行業中一項長期存在的挑戰：客服代表必須應對大量的產品資訊、法規要求和作業指引。能夠即時調出相關資訊的 AI，大大提升了他們的工作效能。
For technology teams, JPMorgan deployed a coding assistant that has been playing a significant role in improving software development efficiency, with the bank seeing 10 to 20 percent productivity increases. Given that Goldman Sachs equipped 12,000 of its developers with generative AI and cites significant productivity gains, this application represents a broad industry trend. Software development represents a particularly strong use case for AI because coding involves translating requirements into logical sequences of instructions - precisely the kind of pattern-matching and generation task where language models excel.
對技術團隊來說，摩根大通導入了一項程式碼助理，這對提升軟體開發效率發揮了舉足輕重的作用，該銀行的生產力提升了 10% 至 20%。高盛為其 12,000 名開發人員配備生成式 AI，並報告顯著的生產力增長，這應用反映出產業的廣泛趨勢。軟體開發是 AI 的一個極具代表性的應用場景，因為編碼本質上是將需求轉化成邏輯指令序列，這正是語言模型擅長的模式匹配和內容生成任務。
The most ambitious aspect of JPMorgan's initiative involves the transition from generative AI that creates content to agentic AI that executes processes. According to an internal roadmap, JPMorgan is now early in the next phase of its AI blueprint, having begun deploying agentic AI to handle complex multistep tasks for employees, with these agents becoming increasingly powerful in their capabilities and connectivity throughout the institution. This transition represents a fundamental escalation in AI's role, moving from assisting humans to autonomously executing tasks.
摩根大通專案中最具野心的部分，是從可以產生內容的生成式 AI，轉向能夠執行流程的代理型 AI。根據其內部藍圖，摩根大通目前正處於 AI 發展的下一階段初期，並已開始部署代理型 AI，為員工處理複雜的多步驟任務，這些代理將隨著能力提升而遍布組織各層面。這一轉變顯示 AI 角色的根本性躍升，從協助人類進入能自動執行任務的階段。
The vision extends to complete organizational integration. JPMorgan's broad vision is for a future where the bank is a fully AI-connected enterprise, with every employee provided with AI agents, every behind-the-scenes process automated, and every client experience curated with AI concierges. Realizing this vision, however, faces substantial obstacles. Even with an $18 billion annual technology budget, it will take years for JPMorgan to realize AI's potential by stitching the cognitive power of AI models together with the bank's proprietary data and software programs, with thousands of different applications requiring significant work to connect into an AI ecosystem.
這個願景延伸至整體組織整合。摩根大通的宏觀目標是打造未來全面由 AI 連結的企業，每位員工都配置專屬 AI 代理，幕後流程全自動化，每位客戶的體驗都由 AI 禮賓服務精心設計。然而，要實現如此願景仍面臨諸多阻礙。即便擁有 180 億美元的年度科技預算，摩根大通仍需花費多年，才能將 AI 模型的認知能力與銀行自有資料和軟體有效結合。數以千計的應用程序必須花大量心力來串連到完整的 AI 生態系統中。
The financial impact of JPMorgan's AI investments has begun materializing. The bank's first-quarter earnings in 2025 reflected the strategic importance of these innovations, reporting net income of $14.6 billion, up 9 percent year-over-year, with investments in AI and technology cited as major contributors to this performance. This validates the business case for AI transformation, demonstrating that the technology delivers measurable value rather than merely consuming resources in pursuit of speculative benefits.
摩根大通在 AI 領域的投資已開始產生財務成效。根據 2025 年第一季財報，該銀行淨利達 146 億美元，年增 9%，而 AI 與科技投資則被列為主要推動力量。這也驗證了 AI 轉型的商業論點，證明這項技術帶來的是可衡量的價值，而非徒然消耗資源追求遙不可及的效益。
JPMorgan's approach offers important lessons about AI transformation at scale. First, the bank prioritized internal, employee-facing applications before launching client-facing AI products. This strategy allows institutions to capture immediate efficiency gains while battle-testing technology in controlled, lower-risk environments. Second, the portal architecture that leverages multiple external models while protecting proprietary data provides a template for other regulated institutions navigating similar security and compliance requirements. Third, the emphasis on comprehensive integration rather than isolated pilot projects reflects recognition that AI's greatest value emerges from system-wide deployment rather than point solutions.
摩根大通的做法為大規模 AI 轉型提供了重要啟示。首先，該銀行優先投入面向員工的內部應用，之後才推出面向客戶的 AI 產品。這策略讓組織能在可控、低風險環境下先行驗證技術，同時立即獲取效率提升。其次，採用能同時串連多個外部模型並保護專有資料的入口架構，為其他受監管機構處理類似安全和合規要求提供了範本。第三，強調全面整合，而非孤立的試點專案，反映出認知到 AI 最大價值來自全系統部署，而非點狀解決方案。
Transformation Across Banking Domains
跨銀行領域的轉型
Understanding how AI reshapes banking requires examining specific domains where the technology's impact manifests most dramatically. Each area of banking operations presents distinct challenges and opportunities for AI transformation.
要理解 AI 如何重塑銀行業，必須檢視科技影響最顯著的不同領域。各銀行業務範疇都有其獨特的轉型挑戰與機遇。
Investment Banking: From Analyst Armies to AI Augmentation
投資銀行：從分析師大軍到 AI 強化
Investment banking traditionally operated through a hierarchical model where junior analysts performed grunt work - building financial models, creating presentations, conducting research - while senior bankers focused on client relationships and deal structuring. AI fundamentally disrupts this model by automating much of the analytical drudgery while augmenting strategic decision-making.
過去，投資銀行運作靠階層體系，初級分析師負責大量粗重工作──建立財務模型、製作簡報、進行研究，資深銀行家則專注於客戶關係與交易結構設計。AI 根本性地打破了這種模式，能自動化大部分重複性分析工作，並強化策略決策。
JPMorgan's demonstration of creating investment banking presentations in 30 seconds illustrates this transformation. The implications extend beyond simple time savings. Investment banks have long faced criticism for brutal junior analyst working conditions, with 80 to 100 hour weeks common for entry-level employees. If AI can handle tasks that previously consumed thousands of analyst hours, banks face decisions about workforce sizing and the traditional apprenticeship model where junior analysts learn by doing extensive analytical work.
摩根大通展示 30 秒產出投資銀行簡報的能力，正好說明了這波變革。其影響不僅止於節省時間：投行一直因初級分析師過勞而備受詬病，每週 80 至 100 小時工時極為常見。如果 AI 能夠處理以往需耗費分析師數千工時的工作，銀行勢必得重新思考人力配置，以及靠大量基礎分析工作培育新手的學徒體制。
AI's capabilities in this domain continue expanding. The systems can now analyze earnings reports, synthesize market research, build comparable company analyses, and generate initial drafts of pitch materials. They can scan news feeds for relevant information about clients and prospects, monitor regulatory filings for material changes, and flag potential deal opportunities based on pattern recognition across vast datasets.
AI 在這領域的能力持續擴展。系統現在可以分析財報、整合市場研究、建立同業比較分析、撰寫簡報初稿。它們也能掃描新聞動態，蒐集與客戶及潛在對象相關的資訊，監控法規文件以掌握重大變動，並透過海量資料辨識潛在的交易機會。
The strategic implications reach beyond efficiency. Investment banks compete largely on the depth of their industry knowledge, the sophistication of their analysis, and the speed with which they can respond to client needs. AI that rapidly synthesizes information across multiple sources and generates sophisticated analysis could compress the timeline for deal processes, raise analytical quality, and enable smaller teams to compete with larger institutions that traditionally wielded advantages through analyst armies.
其戰略意涵遠超過單一效率提升。投資銀行主要在於產業知識深度、分析精細度和快速因應客戶需求的能力而競爭。若 AI 能高效率匯整各方資訊並產出高階分析，將能壓縮交易流程時程、提升分析品質，並讓小型團隊有能力挑戰過去倚賴分析師大軍的業界巨擘。
However, investment banking also illustrates AI's current limitations. Deal-making fundamentally involves judgment calls about valuation, timing, competitive dynamics, and client relationships. While AI can inform these decisions by analyzing relevant data and generating options, the ultimate choices require human judgment shaped by experience, intuition, and interpersonal understanding that current AI systems lack. The most successful firms will likely be those that most effectively blend AI's analytical capabilities with human strategic insight.
但投資銀行也顯示出 AI 目前的局限。交易本質上需判斷評價、時機、競爭態勢與客戶關係。雖然 AI 可分析資訊以協助決策並提出選項，最終抉擇仍需仰賴經驗、直覺以及人際理解，而這些是現行 AI 系統欠缺的。最成功的企業，很可能是那些能最有效結合 AI 分析力與人類策略洞察的公司。
Retail and Consumer Banking: Personalization at Scale
零售與消費金融：大規模個人化
Retail banking faces different challenges than investment banking. Rather than supporting small numbers of high-value transactions, consumer banking handles millions of relatively standardized interactions. AI's ability to deliver personalized experiences at mass scale makes it particularly powerful in this domain.
零售銀行面臨與投資銀行不同的挑戰。消費金融業並非服務少數高價值交易，而是承接數以百萬計的標準化互動。AI 能在大規模上提供個人化體驗，使其在此領域顯得格外強大。
Fraud detection represents one of the most mature AI applications in consumer banking. Traditional rules-based systems flagged transactions that matched predetermined suspicious patterns - large cash withdrawals, international purchases, rapid sequences of transactions. These systems generated many false positives while missing sophisticated fraud schemes. Modern AI systems analyze vast numbers of variables simultaneously, recognize subtle patterns that indicate fraud, and continuously learn from new fraud techniques. JPMorgan uses AI to curtail fraud, and such systems now operate across the industry.
詐欺偵測是消費性銀行業中最成熟的 AI 應用之一。傳統的規則式系統會標註符合預先設下可疑模式的交易，如大額提領、國際消費、短時間內多次交易，但這些系統常產生大量誤報，卻漏掉複雜的詐欺手法。現代 AI 系統能同時分析數以百計變數，識別詐欺細微的徵兆，並持續從新詐欺技術中學習。摩根大通用 AI 抑制詐騙，這類系統現已成業界標配。
Customer service represents another major application domain. Banks like HSBC use generative AI to create customized product recommendations based on individual spending habits. Rather than offering the same credit card or savings account to all customers, AI analyzes individual transaction histories, identifies patterns, and suggests products aligned with specific financial behaviors and needs. This personalization extends to timing - AI can determine optimal moments to present offers when customers are most likely to engage.
客服是另個主要應用領域。像匯豐銀行這樣的機構運用生成式 AI，根據個人消費習慣推薦專屬商品。不再給所有人推薦相同信用卡或儲蓄帳戶，AI 會分析個人交易紀錄、找出行為模式，匹配符合財務需求與行為的商品。這種個人化還延伸到時點──AI 能判斷什麼時刻推出優惠最容易引起消費者注意。
Account management processes that traditionally required extensive human involvement increasingly flow through AI systems. Opening accounts, verifying identities, assessing creditworthiness, and resolving routine issues can all be handled through AI-powered systems with human intervention reserved for edge cases and complex situations. This dramatically reduces operational costs while potentially improving customer experience through faster processing and 24/7 availability.
傳統上須大量人力參與的帳戶管理流程，現在也愈來愈多由 AI 系統承接。開戶、身份驗證、信用評等、處理例行問題，這些都能靠 AI 自動化處理，僅將人力留給特殊或複雜案例。這不僅大幅降低營運成本，也由於處理更快、服務 24 小時，有望提升顧客體驗。
The vision extends to AI-powered financial advisors that provide personalized guidance across the customer base. Banks leverage AI-powered insights to understand customer behavior more deeply, with algorithms analyzing spending patterns and financial behaviors to provide personalized recommendations, and advanced machine learning models assessing risk tolerance through both traditional questionnaires and behavioral data. This democratizes financial planning capabilities that previously required human advisors accessible only to wealthy clients.
這願景還延伸到 AI 金融顧問，針對不同顧客群提供量身打造的理財建議。銀行運用 AI 洞察深入理解顧客行為，讓算法分析消費模式、財務行為並提供專屬建議，高階機器學習模型不只用問卷，還能結合行為資料來判斷風險承受度。這讓以往只有高資產客戶才享有的財富規劃，實現在普羅大眾間普及化。
The consumer banking transformation, however, raises important questions about financial inclusion and algorithmic bias. AI systems trained on historical data can perpetuate or amplify existing disparities in credit access, insurance pricing, and financial services availability. Banks deploying AI in consumer-facing applications must grapple with ensuring their systems treat all customers fairly while remaining profitable businesses.
然而，消費金融的轉型也引發了金融包容性與算法偏見的重要質疑。AI 若以過往資料訓練，容易延續甚至放大原本信貸取得、保險定價、金融服務等的差異。金融機構在面向消費者應用 AI 時，必須努力確保系統既能公平對待所有客戶，又維持其商業獲利。
Risk Management and Compliance: Intelligent Monitoring
風險管理與合規：智能監控
Banking fundamentally involves managing risk - credit risk, market risk, operational risk, liquidity risk, and compliance risk. AI transforms risk management by enabling continuous, comprehensive monitoring at scales impossible for human analysts.
銀行根本上是風險管理產業，包括信用風險、金融市場風險、營運風險、流動性風險及合規風險。AI 讓風險管理變革，可以做到人力難以企及的持續性、全方位監控。
Know Your Customer and Anti-Money Laundering processes exemplify AI's impact on compliance operations. HSBC's AI-powered approach enables the bank to navigate contemporary financial crime complexities by identifying unusual patterns and potentially illegal activities, proving far more effective at distinguishing between normal and suspicious behavior than traditional methods. Traditional compliance systems relied on rules-based screening that
「了解你的客戶 (KYC)」和「防制洗錢 (AML)」流程，正是 AI 對合規作業影響的代表。匯豐銀行的 AI 驅動方法，能夠識別可疑模式及潛在非法活動，使該行在應對現代金融犯罪複雜度時游刃有餘。與傳統規則式篩查相比，這種 AI 方法更能有效區分正常與可疑行為。傳統合規系統則主要仰賴規則篩檢……generated enormous numbers of alerts requiring manual review. Most proved to be false positives, consuming compliance staff time while creating risk that true suspicious activity might be buried in the noise. AI systems apply more sophisticated pattern recognition, learn from feedback about which alerts prove meaningful, and dramatically improve the signal-to-noise ratio.
產生了大量需要人工審查的警示。多數最終證明是誤報，既耗損合規人員的時間，又提高了真正可疑活動被大量無用訊息淹沒的風險。AI 系統運用更先進的模式識別技術，並從哪些警示真正有意義的反饋中學習，大幅提升訊號與雜訊的比例。
Credit risk assessment illustrates how AI enables more nuanced evaluation. Credit risk assessment has evolved from analyzing 8 to 10 variables into a sophisticated system capable of processing over 100 different factors simultaneously. This allows banks to extend credit to customers who might be declined by traditional scoring models while more accurately identifying high-risk borrowers. The implications for financial inclusion are significant - many individuals and small businesses historically denied credit because they don't fit standard profiles may gain access through AI systems capable of recognizing creditworthiness through alternative data and more sophisticated analysis.
信貸風險評估正好說明了 AI 如何實現更細緻的判斷。信貸風險評估已從僅分析 8 至 10 個變數，進化為能同時處理超過 100 種不同因素的先進系統。這讓銀行能夠向傳統評分模型本會拒絕的客戶提供信貸，同時更精確地辨識高風險借款人。這對於金融普惠具有重大意義——許多過去因不符標準範疇而被拒貸的個人和小型企業，現在可透過能辨識替代數據和運用進階分析的 AI 系統取得信貸。
Market risk management benefits from AI's ability to process vast amounts of market data, news, and social media sentiment in real-time, identifying correlations and predicting volatility patterns that inform trading positions and hedging strategies. AI analytics tools process market data faster and more accurately than humans, spotting trends and predicting behavior with superior precision.
市場風險管理則受惠於 AI 能即時處理海量市場數據、新聞和社群媒體情緒，找出相關性並預測波動模式，以協助制定交易部位和避險策略。AI 分析工具能比人工更快、更準確地處理市場數據，發掘趨勢並以更高精度預測行為。
Regulatory compliance increasingly relies on AI to navigate the complexity of financial regulation. Investments like BBVA's stake in Parcha, which builds enterprise-grade AI agents that automate manual compliance and operations tasks including reviewing documents, extracting data, and making decisions on onboarding, compliance, and risk management, illustrate banks' recognition that AI is essential for managing regulatory burdens. The volume of regulatory requirements, the frequency of updates, and the need to apply rules consistently across thousands of transactions make compliance a natural fit for AI.
合規監管工作愈來愈高度依賴 AI 以應對金融監理的複雜性。像 BBVA 投資 Parcha 等專為企業級 AI 代理人打造的項目——這些 AI 可自動化處理手動合規和營運任務，包括審閱文件、擷取數據、決策開戶、合規與風控——顯示銀行已認知到 AI 在管理監理負擔上的關鍵性。龐大的監管需求、頻繁的規則更新，以及必須在成千上萬筆交易中一致套用規範，使合規工作與 AI 的結合成為最佳選擇。
Treasury Operations and Trading: Speed and Precision
交易是金融業最早也是應用最廣泛的 AI 場域之一。多年來，演算法交易已主導股票市場，AI 系統以微秒等級速率執行交易、管理複雜投資組合、並比任何人類交易者更快發現套利機會。
現今的 AI 浪潮已超越傳統演算法交易，邁向更進階的應用。AI 系統如今結合自然語言處理來分析財報電話會議逐字稿、新聞文章，以及社群媒體訊息，以捕捉能驅動市場的情緒信號。同時應用機器學習分析委託單流量中的隱含機構部位，並以市場微結構分析優化交易策略，兼顧流動性、波動度和多市場的交易成本。
出納業務則受惠於 AI 優化流動性管理的能力，能預測全行現金流、決定資本最佳運用方案，以及高效率管理擔保品需求。這些後台作業雖缺乏前線交易的光環，卻包含龐大的操作複雜度與優化空間。
AI 驅動的交易競爭態勢創造出科技軍備競賽。能部署更先進 AI、存取更高品質數據或達到更快執行速度的機構，將直接在獲利上取得優勢。這促使金融業持續投資 AI 能力與基礎設施，預計銀行在 AI 項目的支出將從 2024 年的 60 億美元增至 2025 年的 90 億美元，2030 年甚至可能高達 850 億美元。
Operations: The Invisible Transformation
銀行作業——包括清算交易、對帳、付款處理和系統維護的幕後工作——正是 AI 驅動效率提升最大的一塊。這些領域僱用大量從事重複性、規則式工作的員工，而這正是 AI 日益擅長的領域。
AI 自動化已讓如富國銀行這樣的金融機構，將常規銀行作業成本降低 25% 至 30%，例如運用 AI 自動化房貸流程，每年省下數百萬營運成本。花旗則表示 AI 使文件處理時間縮短 60%，帶來顯著的成本節省。這些效率提升不僅反映在成本降低，也體現在流程加速、錯誤減少及改善客戶體驗上。
這對銀行內部作業職位的人力需求有深遠影響。這些工作正是 AI 擅長自動化——高量、規則式、重複性且需準確但不需創造性解決問題或複雜判斷的任務。隨著自動化消除現有數十萬崗位，銀行勢必面對如何管理人力轉型的艱難議題。
Agentic AI: The Decisive Technological Shift
要真正了解現今 AI 轉型與以往銀行自動化浪潮根本上的不同，必須從具代理性(agentic)的 AI 來看——這類系統能在極少甚至無須人類監督的情況下，自主進行多步推理與行動。這代表技術層面由過去舊式 AI 的量變，躍升至質變。
傳統銀行自動化仰賴預定規則。系統或自動標記超出特定門檻的交易，但如何處理仍需由人工判定。或能將客戶詢問導向適當部門，但真實應對仍靠員工。系統也能產生標準報表，但內容解讀與決策則靠人工。這些系統遵循流程腳本，任何跳脫預設腳本都必須人工介入。
具代理性的 AI 則完全不同。這類系統能根據自身判斷，自主規劃執行一連串行動以實現目標，並針對每個階段自行決策、依據成果適應策略。他們的運作方式更類似賦予大方向的員工，由其自行思考如何完成任務，而不只是傳統執行明確指令的軟體。
支撐代理型 AI 的技術能力來自大型語言模型的突破。這些模型展現某種近似一般推理的能力——能理解複雜指令、拆解問題、制定計劃並評估選項。結合工具使用與數據存取能力後，這些系統就能展現高階自主行為。
以投資銀行場景為例。傳統自動化僅能依預設模板與數據來源產生標準財務分析。代理型 AI 則可接受像「準備一份與潛在收購標的開會的簡報」這種高階指令，然後自動決定要收集哪些資訊、哪種分析最為重要、應該比較哪些項目、以及如何組織簡報架構。每一步都會自我推理與選擇，而不需要明確的人為指示。
Capital One 是公開 Chat Concierge 代理型 AI 應用的少數銀行之一，並計劃以同一架構開發更多內部工具。此系統展現了代理型 AI 如何自動處理複雜的客戶服務互動——能理解多輪對話中的意圖、主動蒐集資料、並採取行動解決問題而無需人為介入。
廣泛部署代理型 AI 所帶來的影響遠超效率提升這麼簡單。它們從根本上改變了工作的本質，將人類從執行者轉變為管理目標與監督工作的角色。投資銀行分析師花在建立模型的時間變少，轉而提出問題或評估 AI 產出的分析；合規專員不再逐筆審查交易，而是設立 AI 監控參數，再處理 AI 標記的案例；交易員從下單執行轉為規劃策略，讓 AI 代理人負責落實。
這種轉變同時帶來機會與挑戰。一方面，人類可擺脫繁瑣任務，專注於更需創意、判斷與人際技巧的高附加價值工作。另一方面，過去以這類任務為主的員工，將面臨職位被代理型 AI 取代的威脅。代理型 AI 部署尚未普及前，領先採用者將獲得明顯競爭優勢，造成產業分化。
這項技術同時也引發控制權與責任歸屬的重大議題：若代理型 AI 執行任務導致不良結果，究竟該由誰負責？是設定目標的員工、部署系統的金融機構，還是研發底層 AI 的工程師？傳統責任觀假設可以檢視人類決策者的判斷並要求其承擔後果，但代理型 AI 使責任分散於人機協同架構，個人責任因此變得模糊。
隨著越來越強大的代理型 AI 被串聯進如摩根大通（JPMorgan）等組織且賦予更多責任，如何確保其可靠性、資安和透明性亦將變得至關重要。as these agents make more consequential decisions, requiring robust governance frameworks, continuous monitoring, and ethical guardrails to manage risk and compliance. Banks deploying agentic AI must develop new governance frameworks that account for autonomous AI decision-making while maintaining accountability and regulatory compliance.
隨著這些智能代理做出越來越重大的決策，必須有健全的治理架構、持續監控與道德防線來管理風險和合規性。部署自主型AI的銀行必須建立新的治理架構，能夠涵蓋自主AI的決策，同時維持問責和法規遵循。
Workforce Disruption: Beyond Automation Anxiety
勞動力衝擊：超越自動化焦慮
The employment implications of AI banking transformation extend far beyond simple automation displacing workers. The impact manifests through complex dynamics involving workforce composition changes, skill requirement shifts, geographic labor distribution, and fundamental questions about the future nature of banking employment.
AI驅動銀行業轉型對就業的影響遠不只於簡單的自動化取代人力。這種衝擊反映於勞動力結構的變化、技能需求的調整、地理勞動分布的轉移，以及對銀行業未來就業本質的根本疑問。
The Displacement Reality
職位被取代的現實
Citigroup published a research report predicting artificial intelligence will displace 54 percent of jobs in the banking industry, more than in any other sector, and a Bloomberg Intelligence report found that global banks are expected to cut as many as 200,000 jobs in the next three to five years as AI takes on more tasks. These projections reflect the reality that banking employs enormous numbers of people in roles involving information processing, analysis, and decision-making - precisely the kinds of tasks where AI systems demonstrate increasing competence.
花旗集團發表的研究報告預測，人工智慧將取代銀行業54%的工作崗位，比例高於其他任何產業。根據彭博智能的報告，隨著AI承擔更多任務，全球銀行預計在未來三到五年內將裁員多達20萬人。這些預測反映出銀行業在資訊處理、分析和決策等崗位雇用了大量人力，而這正是AI系統越來越擅長的領域。
JPMorgan's consumer banking chief told investors that operations staff would fall by at least 10 percent, providing specific indication of the scale of workforce reduction that even leading institutions expect. The impact falls unevenly across roles. Those at risk of having to find new roles include operations and support staff who mainly deal in rote processes like setting up accounts, fraud detection, or settling trades, while the shift favors those who work directly with clients like private bankers with rosters of rich investors, traders who cater to hedge fund and pension managers, or investment bankers with relationships with Fortune 500 CEOs.
摩根大通的消費金融部門主管向投資人表示，作業人員的數量至少會減少10%，這明確顯示即使是龍頭機構也預期將大幅縮編。受影響最大職類集中在主要處理例行流程的營運與支援人員，如開設帳戶、防詐偵測、交易結算等，這些崗位的人將不得不尋找新工作。相對來說，直接與客戶互動的職務則受到青睞，例如服務高資產客戶的私人銀行家、負責對接對沖基金與退休基金經理的交易員、以及與財富500強公司CEO維持關係的投資銀行家。
This creates a bifurcation in banking employment. High-skill, client-facing roles that require relationship management, strategic judgment, and interpersonal skills remain valuable and may even become more valuable as AI handles supporting analytical work. Middle-skill roles involving standardized information processing and analysis face the greatest displacement risk. Entry-level positions that traditionally served as training grounds for careers in banking may largely disappear, raising questions about how institutions develop future senior talent.
這造成銀行業就業的分化。需要關係管理、策略判斷與人際溝通的高技能且面對客戶的職務依然重要，甚至隨著AI承擔分析性工作，這些職位的價值將進一步提升。從事標準化資訊處理與分析的中階技術職最容易被取代。過去用來培養未來資深人才的初階職位可能大幅消失，這也讓業界如何培養未來幹部的議題更加複雜。
Dario Amodei, chief executive of AI firm Anthropic, said nearly half of all entry-level white-collar jobs in tech, finance, law, and consulting could be replaced or eliminated by AI. This projection directly challenges the traditional career development model in professional services where junior employees learn by performing routine tasks under senior supervision. If AI eliminates these entry-level roles, institutions must develop alternative pathways for developing expertise and advancing careers.
AI公司Anthropic執行長Dario Amodei表示，科技、金融、法律與顧問等領域，將近一半的初階白領職位可能被AI取代或消失。這個預測對於傳統的專業服務人才培養模式帶來直接的挑戰——新人過去通常是在資深員工監督下靠例行性工作學習。如果AI淘汰這些初階職務，各機構就必須發展替代的專業養成與升遷路徑。
Retraining: Promise and Limits
再培訓：承諾與極限
A Federal Reserve Bank of New York survey found that rather than laying off workers, many AI-adopting firms are retraining their workforces to use the new technology, with AI more likely to result in retraining than job loss for those already employed, though AI is influencing recruiting, with some firms scaling back hiring due to AI and some firms adding workers proficient in its use. This suggests institutions recognize the value of retaining experienced employees and helping them adapt to new roles rather than simply replacing them with AI.
紐約聯邦準備銀行調查發現，許多導入AI的企業選擇為現有員工重新培訓，使他們能夠運用新科技，而非直接裁員。對已雇用員工而言，AI帶來培訓機會多於失業，但AI同時正影響招募，有些公司因AI縮減新聘人力，有些則積極吸收精通AI的新員工。這反映出各機構開始重視留才價值，傾向協助現有人員轉型適應新角色，而非單純用AI取而代之。
However, research on retraining effectiveness paints a more sobering picture. Job-training programs under the Workforce Innovation and Opportunity Act generally lead to increased earnings for displaced workers, but those entering high AI-exposed occupations see smaller gains - about 25 to 29 percent less - than those targeting low AI-exposed roles, with only certain fields such as legal, computation, and arts showing high potential for retraining into well-paid, AI-exposed jobs. This indicates that while retraining helps, it may not fully compensate workers displaced from roles eliminated by AI.
然而，針對再培訓效果的研究則較為保守。根據美國「勞動力創新與機會法」下的職業培訓計畫，轉業勞工薪資普遍提升。但流向AI曝險度高職業的人，其薪資漲幅比流向AI曝險度低職業者少了約25至29%，只有如法律、電腦與藝術等領域，才有較高機會透過再培訓進入高薪AI相關職缺。因此，再培訓雖有幫助，但難以全然彌補被AI淘汰職務的損失。
The challenge extends beyond individual capability to systemic capacity. The World Economic Forum projects that 92 million jobs will be displaced by 2030 but 170 million new ones will be created requiring new skills. Even if this net-positive scenario materializes, the transition creates enormous friction as displaced workers acquire new skills, geographic labor markets adjust, and institutions adapt to new workforce models. The timeline matters critically - if displacement occurs faster than job creation and retraining, the period of disruption could be painful and prolonged.
這個難題不僅關乎個人能力，更是制度性的挑戰。世界經濟論壇預測，到2030年將有9200萬個工作被取代，但將新增1.7億個需新技能的工作。即便這種總體正向情形成真，過渡期仍會因勞工重新學習技能、地區就業市場調整與機構適應新型態員工等因素而產生巨大摩擦。時程尤為關鍵——如果工作被淘汰的速度快於新職位創造和再培訓，陣痛期將格外漫長且艱苦。
McKinsey Global Institute estimates approximately 375 million workers globally - about 14 percent of the workforce - will need significant retraining by 2030 to remain economically viable, with the speed of current displacement surpassing even those predictions. The scale of this reskilling challenge dwarfs anything attempted in modern economic history, raising serious questions about whether existing training infrastructure can meet the need.
麥肯錫全球研究院估計，到2030年，全球約有3.75億名勞動人口（約占總數14%）需要接受大規模再培訓才能維持經濟競爭力，而目前被淘汰的速度甚至已超過這些預測。這種再培訓規模遠超現代經濟史上的任何嘗試，也讓現有培訓制度是否能趕上需求，成為嚴肅的現實問題。
Geographic Redistribution
地理再分布
AI's impact on banking employment extends to geographic distribution of jobs. Banks have increasingly concentrated back-office operations in lower-cost locations - Bangalore, Hyderabad, Guangzhou, Manila, and other offshore centers. HSBC faces a shortfall of nearly 10,000 desks in locations like Bangalore, Hyderabad, and Guangzhou where technologists and back-office people work, and the bank is in talks with companies to automate back-office functions and reduce its cost base. If AI can perform work previously offshored, the geographic distribution of banking employment could shift significantly, with implications for both developed and developing economies.
AI對銀行業就業版圖也產生影響。銀行業越來越多將後勤作業集中在成本較低的地區，例如班加羅爾、海得拉巴、廣州、馬尼拉等離岸中心。滙豐銀行在班加羅爾、海得拉巴和廣州等地僱用大量科技和後台人力，據稱這些地區的座位缺口近一萬人，目前正與企業洽談進一步自動化相關作業以降低成本。如果AI能夠執行過去外包或離岸的工作，銀行業的就業地理分布將大幅改變，對已開發國家與開發中國家都將產生深遠影響。
This creates complex dynamics. Developing economies have built substantial sectors providing services to multinational banks. If AI displaces this work, it eliminates employment that has lifted millions into middle-class prosperity. Simultaneously, banks may consolidate operations closer to their headquarters if physical headcount becomes less relevant, potentially reversing offshoring trends but creating a smaller absolute workforce.
這帶來複雜的影響。許多開發中國家已建立起為跨國銀行服務的龐大產業。如果這些工作被AI取代，將會失去讓數百萬家庭躋身中產階級的就業機會。與此同時，若實體崗位不再重要，銀行有可能將作業整合回總部，甚至逆轉過去的離岸趨勢，只是整體工作機會總量反而變少。
New Roles and Skills
新職位與新技能
Job displacement represents only part of the employment story. AI also creates new roles that didn't previously exist. As AI systems become more embedded in banking operations, a parallel workforce is emerging to manage, monitor, and refine these technologies, with AI auditors ensuring algorithms operate within regulatory and ethical boundaries, ethics officers evaluating AI models for biases and unintended consequences, and human-AI trainers continuously feeding data to machine learning models and fine-tuning outcomes based on customer behavior.
就業崗位被取代只是故事的一部分。AI同時也創造出以往不存在的新職位。隨著AI越來越深度嵌入銀行運作，出現了新的平行人力——負責管理、監控並優化AI，包括：AI稽核人員確保演算法在法規與道德範圍內運作，道德主管檢查模型偏誤與意外後果，人機共訓員則持續為機器學習模型餵入新資料並根據客戶行為微調結果。
These roles require combinations of domain expertise and technical understanding. An AI auditor working in lending must understand both credit risk evaluation and machine learning model behavior. An ethics officer must comprehend both regulatory compliance and algorithmic bias. These hybrid roles command premium compensation but require skills that few current workers possess, creating talent shortages even as AI displaces workers from other banking roles.
這些新職位需要結合專業知識與技術理解。擔任AI稽核的放款專員，必須同時懂信貸風險與機器學習模型特性。道德主管需了解合規法規並掌握演算法偏見來源。這類跨域職業往往薪資較高，但目前相關人才稀缺，即使AI取代部分崗位，其他領域依然面臨搶才競爭。
The advent of generative AI is like the impact Microsoft Excel had when it came out in 1980, with everyone saying it would eliminate finance people, but instead it changed how they work. This historical analogy suggests AI might ultimately expand banking capabilities rather than simply replacing workers. Excel didn't eliminate financial analysts; it enabled them to perform more sophisticated analyses more quickly, raising expectations for analytical depth and creating demand for analysts who could leverage the tool effectively. AI might follow a similar pattern, with banks that deploy it effectively able to offer more sophisticated services, serve more clients, and ultimately employ substantial workforces in reconfigured roles.
生成式AI的出現就像1980年代Excel問世時，大家以為它會淘汰財務人員，結果只是改變了他們的工作型態。這樣的歷史類比說明，AI最終可能帶來銀行業務能力的擴張，而非只是單純取代人力。Excel並沒有消滅金融分析師，反而讓分析工作更快更深，刺激市場對能善用此工具的專業人才需求。AI可能也將如此——有效使用AI的銀行可以提供更複雜的服務、服務更多的客戶，並在新型態職位下維持大量員工。
The employment transition ultimately depends on how institutions manage change. Banks that invest in comprehensive retraining programs, create pathways for displaced workers to move into new roles, and approach AI deployment as augmenting rather than replacing humans can potentially minimize disruption. Those that pursue AI primarily as a cost-cutting measure through workforce reduction will create more painful transitions for employees while potentially sacrificing institutional knowledge and expertise that proved difficult to replicate with AI alone.
就業轉型最終取決於機構如何因應變革。重視全面性再培訓、協助被取代者轉換跑道、認知AI是輔助而非取代人力的銀行，能夠較有效降低衝擊。反之，把AI視為削減人力成本手段的銀行，往往會帶來痛苦的員工轉型歷程，且失去難以用AI重現的機構經驗與專業知識。
Competitive Dynamics and Strategic Advantages
競爭動態與策略優勢
If JPMorgan can beat other banks to the punch on incorporating AI, it will enjoy a period of higher margins before the rest of the industry catches up. This observation captures the competitive dynamics driving massive AI investments across banking. Early movers gain temporary advantages, but those advantages erode as competitors adopt similar capabilities, eventually pushing the entire industry to higher performance levels that become the new baseline.
如果摩根大通能比其他銀行更早導入AI，該行將在產業跟進之前享有一段毛利增高的紅利期。這正點出銀行業爭相投入AI的競爭本質。先行者可短暫取得優勢，但隨著競爭對手跟進，最終全產業水平被推高，成為新的基準線。
The pattern mirrors previous technology transformations in banking. When ATMs emerged, early adopters gained cost advantages and customer convenience benefits. But ATMs quickly became ubiquitous, and the advantage shifted to banks that deployed them most extensively and integrated them most effectively with broader service offerings. Online banking followed similar dynamics - first-movers gained customer acquisition advantages, but within years, every bank needed online capabilities to compete. AI appears to be following this trajectory, but with potentially more dramatic effects.
這種模式和過去銀行業的技術轉型如出一轍。ATM剛推出時，領先採用者有成本與客戶便利優勢，但很快ATM普及開來，優勢就轉向能廣泛且有效整合此服務的銀行。網銀也是如此——早期先驅搶得市占，數年後所有銀行都必須具備線上能力才能競爭。AI發展似乎也沿著這路徑，但其影響可能更加劇烈。
Several factors determine which institutions gain the most from AI investments. First, scale matters enormously. JPMorgan's $18 billion annual
（剩餘部分請補充原文，以便繼續翻譯。）technology budget enables investments that smaller institutions cannot match. Building sophisticated AI systems, assembling specialized talent, and integrating AI across vast operational infrastructure requires resources that favor the largest banks. This could accelerate industry consolidation as smaller banks struggle to keep pace with AI-powered competitors.
科技預算使大型機構能夠進行小型機構無法比擬的投資。建構先進的人工智慧系統、招攬專業人才，以及將AI整合進龐大的營運基礎設施，都需要大量資源，而這些資源大多集中在最大型的銀行。隨著小型銀行難以追上導入AI的競爭者，這可能加速產業整併趨勢。
Second, data advantages create compounding returns. AI systems improve through exposure to more data, and larger banks process more transactions, serve more customers, and operate in more markets than smaller institutions. This data richness enables more sophisticated AI that delivers better customer experiences, attracts more customers, and generates more data - a reinforcing cycle that advantages incumbents with established customer bases over new entrants.
其次，數據優勢帶來複利效應。AI系統透過處理更多數據而進步，而大型銀行的交易量、客戶數量及市場覆蓋範圍都遠超小型機構。這種數據豐富性讓AI更加精進，進而提供更佳的客戶體驗，吸引更多客戶，產生更多數據—形成一個強化既有業者、特別是已建立穩固客群的銀行相較於新進者的正向循環。
Third, legacy infrastructure both constrains and shapes AI deployments. Banks operate on technology stacks accumulated over decades, with critical systems running on mainframes alongside modern cloud applications. There is a value gap between what the technology is capable of and the ability to fully capture that within an enterprise, with companies working in thousands of different applications requiring significant work to connect those applications into an AI ecosystem and make them consumable. Institutions with more modern infrastructure can deploy AI more rapidly and comprehensively than those wrestling with complex legacy systems.
第三，舊有基礎設施既限制也影響著AI的部署。銀行的技術架構是經過數十年逐步累積而來，關鍵系統多數運行在主機(Mainframe)，同時也有現代雲端應用並存。現今科技的能力與企業能否完全應用之間存在價值落差，尤其是在企業內部有成千上萬的應用程式需要大幅整合到AI生態圈並使其可用。擁有較現代化基礎設施的機構能夠更快速、全面地部署AI，而還在與複雜舊系統奮戰的機構則進度較慢。
Fourth, regulatory compliance capabilities matter increasingly. Banks operate in heavily regulated environments where deploying new technology requires demonstrating that it meets regulatory requirements for transparency, fairness, security, and reliability. Institutions with sophisticated compliance frameworks and strong regulatory relationships can navigate AI deployment challenges more effectively than those with weaker compliance capabilities.
第四，合規能力越來越重要。銀行是在高度受管制的環境下運作，推行新技術必須證明能符合監管機關對於透明、平等、安全及可靠性的要求。擁有成熟合規架構且與監管單位關係深厚的機構，更能有效因應AI部署過程中的種種挑戰，相較於合規能力較弱的單位更具優勢。
Industry structure influences how AI advantages manifest. In highly commoditized banking services - payments processing, basic deposit accounts, simple loans - AI-driven efficiency advantages translate primarily into cost reductions that either improve margins or enable price competition. In differentiated services - wealth management, investment banking, sophisticated corporate banking - AI can enable service enhancements that support premium pricing and market share gains.
產業結構形塑了AI優勢的展現方式。像是支付處理、基本存款帳戶、簡單貸款等高度商品化的銀行服務，AI帶來的效率優勢主要反映在降低成本、提升利潤率或價格競爭上。而在財富管理、投資銀行及複雜企業金融等差異化服務領域，AI則能夠促使服務升級，支撐高價收費與市佔成長。
Citigroup armed 30,000 developers with generative AI coding tools and rolled out a pair of generative AI-powered productivity enhancement platforms to its broader workforce, while Goldman Sachs has furnished roughly 10,000 employees with an AI assistant and expects to complete companywide rollout by year's end. These deployments by JPMorgan's major competitors indicate the AI transformation has become imperative across the industry. No major bank can afford to ignore AI, and competitive dynamics ensure that AI investments will continue accelerating.
花旗銀行已為三萬名開發人員配備生成式AI程式設計工具，並向全體員工推出兩款生成式AI生產力提升平台；高盛則為約一萬名員工配備AI助理，並預計年底前全公司皆會導入。這些摩根大通主要競爭對手的AI部署顯示，AI轉型已是全行業的必然趨勢。任何一家大型銀行都無法忽視AI的影響，競爭態勢也確保了AI投資只會持續加速。
The geographic dimension of competition adds complexity. Bank of America is spending $4 billion on AI and new technology initiatives in 2025, accounting for nearly one-third of its $13 billion technology cost line. American banks face competition not only from each other but also from European institutions, Asian banks, and potentially Big Tech firms that might expand into financial services. Chinese banks deploy AI extensively in mobile payments and lending, European banks face regulatory pressures that both constrain and shape AI deployment, and Asian institutions like DBS and HSBC pursue aggressive digitalization strategies.
地理層面的競爭使情勢更為複雜。美國銀行2025年在AI及新科技計畫上的花費預計高達40億美元，約等於其技術總開支的三分之一。美國銀行不僅彼此競爭，也面臨來自歐洲銀行、亞洲銀行，甚至有可能進軍金融業的大型科技公司挑戰。中國銀行在行動支付與貸款領域廣泛應用AI，歐洲銀行則因監管壓力而在AI部署既受限又被重新塑造，像星展銀行及匯豐等亞洲機構則積極推動數位化轉型。
Big Tech represents a particularly interesting competitive dynamic. Companies like Google, Amazon, and Microsoft possess world-leading AI capabilities, vast computational resources, and enormous user bases. While regulatory restrictions have historically limited their expansion into core banking, they increasingly offer financial services at the margins - payments, lending, financial planning. If regulators allow deeper Big Tech participation in banking, AI-powered platforms operated by technology giants could disrupt traditional banking business models fundamentally.
大型科技公司帶來特別不一樣的競爭態勢。Google、Amazon、Microsoft等企業擁有全球頂尖AI實力、龐大的運算資源與大量用戶群。雖然過往監管限制這些公司切入核心銀行業，但他們近年來已開始在支付、放貸、理財等周邊金融服務陸續布局。倘若未來監管單位放寬限制，大型科技公司透過AI驅動的平台將對傳統銀行的商業模式帶來根本性衝擊。
The ultimate competitive outcome remains uncertain. AI might amplify advantages held by the largest, most sophisticated institutions, leading to industry consolidation. Alternatively, AI could lower barriers to entry by enabling smaller institutions to deliver sophisticated services without massive human workforces, promoting competition. Most likely, the industry will bifurcate, with a small number of massive, AI-powered universal banks competing against specialized institutions that use AI to excel in specific niches.
最終的競爭格局尚未明朗。AI可能讓最大、最先進的機構優勢進一步擴大，促使產業整合趨勢。不過，AI也能降低進入門檻，讓小型機構不必依靠龐大人力即能提供先進服務，有助於促進競爭。較為可能的是產業將走向二元分化：少數龐大、AI化的全方位銀行對上運用AI專精特定利基領域的專業化機構。
Implementation Realities: The Value Gap Challenge
實際執行現實：價值落差的挑戰
There is a value gap between what the technology is capable of and the ability to fully capture that within an enterprise, with companies working in thousands of different applications requiring significant work to connect those applications into an AI ecosystem and make them consumable. This observation by JPMorgan's chief analytics officer captures the central challenge in AI banking transformation: the technology's potential vastly exceeds what institutions can currently implement.
科技的能力與企業能否完全發揮之間存在價值落差。企業常需在成千上萬個不同應用中運作，因此必須投入大量工程將這些應用程式串接成AI生態圈並讓其可用。摩根大通首席分析長的這番觀察點出了AI在銀行業轉型的核心挑戰：科技潛能遠大於現今機構能落實的程度。
Several factors create this value gap. First, legacy infrastructure presents massive integration challenges. Banks operate critical systems dating to the 1960s and 1970s, written in COBOL and running on mainframes. These systems handle functions like account management, transaction processing, and payment clearing where any failure could be catastrophic. Connecting them to AI systems requires extensive interface development, rigorous testing, and careful risk management.
多項因素造成這個價值落差。首先，舊有基礎設施帶來龐大整合挑戰。銀行的關鍵系統往往可追溯至1960～1970年代，採用COBOL撰寫並運行於主機(Mainframe)之上，處理帳戶管理、交易處理及清算等重要功能，任何失誤都可能釀成災難。將這些舊系統串連進AI架構中，需耗費大量介面開發、嚴格測試及謹慎風險控管。
The complexity multiplies because banks don't operate on unified platforms but rather on collections of hundreds or thousands of distinct applications accumulated through decades of organic development, mergers and acquisitions, and technological evolution. Each application has its own data formats, business logic, and interfaces. Creating an AI layer that can interact with all these systems coherently represents an enormous engineering challenge.
這種複雜性更加劇，因為銀行不是在統一的平台上營運，而是長年透過有機發展、併購及技術演進，逐步累積上百甚至上千個各自獨立的應用程式。每個應用程式都有自己的資料格式、商業邏輯與介面。要打造一個能與所有系統協同運作的AI層，絕對是巨大的工程挑戰。
Second, data quality and accessibility issues limit AI effectiveness. AI systems require clean, structured, consistent data to function well. Banks' data resides across innumerable systems in incompatible formats with inconsistent definitions, incomplete records, and quality problems accumulated over decades. Before AI can deliver its potential, institutions must undertake massive data remediation efforts - standardizing formats, resolving inconsistencies, establishing data governance, and building pipelines that make data accessible to AI systems.
其次，資料品質與可存取性問題也限制了AI的效益。AI必須仰賴乾淨、有架構且一致的數據才能正常運作。銀行的資料分散於無數系統中，格式不相容、定義不一致、不完整，且累積多年品質問題。在AI能發揮潛能前，機構必須先進行大規模資料修復，包括標準化格式、消弭不一致、建立資料治理制度，以及打造讓資料可供AI調用的數據管線。
Third, organizational resistance slows implementation. AI transformation requires changing how people work, which business processes flow, and who holds decision-making authority. These changes threaten existing power structures, require learning new skills, and create uncertainty about job security. Even when leadership commits to AI transformation, middle management resistance, employee anxiety, and simple inertia can dramatically slow implementation.
第三，組織抗拒拖慢導入進度。AI轉型牽涉到改變人員工作方式、業務流程甚至決策權的分配。這些變動威脅原有權力結構，需要員工學習新技能，也可能引發工作保障的不安。即使高層全力支持，部門主管的阻力、員工的焦慮與惰性，也可能大大延緩執行進度。
Fourth, talent scarcity constrains deployment speed. JPMorgan employs more AI researchers than the next seven largest banks combined, but even JPMorgan faces talent constraints. The number of people who understand both advanced AI and banking operations remains limited relative to industry needs. This talent shortage drives up compensation costs and limits the pace at which institutions can expand AI capabilities.
第四，AI人才短缺限制佈署速度。即便摩根大通聘有超過其餘七家大型銀行總和的AI研究人員，該公司也仍受到人才瓶頸。能同時精通先進AI技術與銀行業務運作的人才，數量遠難滿足整體產業需求。這種人才短缺推升薪酬成本，也限制機構擴充AI能量的節奏。
Fifth, regulatory uncertainty complicates planning. Banks must satisfy regulators that their AI systems operate safely, fairly, and transparently. However, regulatory frameworks for AI in banking remain under development, creating uncertainty about what requirements institutions must meet. This uncertainty makes banks cautious about deploying AI in ways that might later prove non-compliant, slowing adoption.
第五，監管不確定性增加規劃難度。銀行必須向監管機關證明AI系統的運作是安全、公正且透明的。然而，銀行AI監管規則尚在制訂中，導致合規標準無法明確，機構在推動AI佈署時不得不謹慎保守，以免日後違規，進而拖慢AI普及速度。
JPMorgan Chase builds its AI foundation on AWS, pushing the AWS SageMaker machine learning platform and AWS Bedrock generative AI platform beyond experimentation into production applications, with 5,000 company employees using SageMaker and more than 200,000 employees now using LLM Suite. This partnership approach - leveraging cloud infrastructure and AI platforms from technology providers rather than building everything internally - helps address some implementation challenges by providing scalable infrastructure and reducing the burden of maintaining AI development platforms.
摩根大通在AWS雲端之上建立AI基礎，將AWS SageMaker機器學習平台與AWS Bedrock生成式AI平台應用於生產環境，現已有5,000名員工使用SageMaker，逾20萬人採用LLM Suite。這種與科技供應商結盟、善用雲端與AI平台（而非完全自建）的策略，有助於解決部分佈署難題，因其提供可擴展的基礎設施並減輕機構維護AI開發平台的負擔。
The organizational dimension of implementation presents perhaps the greatest challenge. Chase is going with a "learn by doing" approach for generative AI, wanting tools in employees' hands with a belief that there is no better way to learn than by actually utilizing the tools, and the bank has been reported to have 450 proofs of concept in the works, a number expected to climb to 1,000. This grassroots approach recognizes that successful AI transformation requires cultural change, not just technology deployment. Employees must understand AI capabilities, identify opportunities for application, and integrate AI into daily workflows. This learning-by-doing approach takes time but builds sustainable capabilities.
執行的組織層面或許最具挑戰性。摩根大通採取「做中學」策略推廣生成式AI，讓員工實際操作工具，相信這是學習的新技術最有效的方法。該行據報已有450項概念驗證(PoC)在進行，預期將增至1000項。這種由下而上的做法認為，成功的AI轉型不僅僅是技術佈建，更需企業文化轉型。員工必須真正了解AI功能、主動尋找應用場景，並將AI融入日常工作流程。「做中學」雖需時間，但能建立長期永續能力。
The financial dimension complicates implementation. Banks' spending on AI initiatives is predicted to increase from $6 billion in 2024 to $9 billion in 2025, and potentially as much as $85 billion in 2030. These investments must be justified through clear return-on-investment cases, but AI's benefits often materialize over years through cumulative efficiency gains, improved decision-making, and enhanced customer experiences that prove difficult to quantify precisely. Institutions face pressure
財務層面也讓執行變得更複雜。預估銀行在AI相關計畫上的支出，將從2024年的60億美元增至2025年的90億美元，甚至於2030年上看850億美元。這些投資必須靠明確的投資報酬率來證明其合理性，但AI的效益往往透過多年的效率提升、決策品質改善以及顧客體驗優化慢慢累積浮現，很難以精準數字立即量化。機構因此承受不小壓力。to demonstrate results while pursuing transformations that require sustained investment before full benefits emerge.
在追求需要持續投入的轉型過程中，必須展現成果，即使這些轉型必須待長期投入後才能顯現全面效益。
The testing and validation challenge for AI systems exceeds that of traditional software. Traditional software follows deterministic logic - given the same inputs, it produces the same outputs, making testing straightforward. AI systems, particularly those using advanced machine learning, behave probabilistically and can produce different outputs for the same inputs. Testing must evaluate not just whether the system works correctly for known cases but whether it generalizes appropriately to novel situations, handles edge cases safely, and degrades gracefully when encountering inputs outside its training distribution.
AI系統的測試與驗證難度遠高於傳統軟體。傳統軟體遵循決定性邏輯——給定相同的輸入，必然產生相同的輸出，使得測試變得直接明瞭。AI系統，特別是採用先進機器學習的系統，則具有機率性行為，對相同輸入可能產生不同的輸出。測試不僅需要評估系統在已知情境下是否運作正確，還必須判斷其是否能適當泛化到新情境，能否安全處理極端案例，以及在遇到超出訓練資料分布的輸入時，是否會逐步衰退且不致於失控。
These implementation challenges explain why AI banking transformation proceeds gradually despite enormous potential. Institutions must balance moving quickly enough to capture competitive advantages against moving carefully enough to manage risks and ensure reliable operations. The tension between speed and caution shapes deployment strategies, with most banks pursuing parallel approaches that layer AI capabilities atop existing systems rather than attempting to rebuild core banking infrastructure from scratch.
這些導入挑戰解釋了為什麼AI在銀行業的轉型雖然潛力巨大，卻推進得很緩慢。金融機構必須在行動足夠迅速以爭取競爭優勢，以及審慎行事控制風險並確保營運可靠之間取得平衡。速度與謹慎之間的拉鋸，影響了部署策略：多數銀行傾向以平行方式在既有系統上增添AI能力，而不是從零重建核心銀行基礎設施。
Risks, Ethics, and Regulatory Gaps
AI banking transformation raises profound questions about safety, fairness, accountability, and social impact that regulators, banks, and society must address. These concerns span technical, ethical, legal, and political dimensions.
AI於銀行業的轉型，帶來了安全、公平、責任歸屬及社會影響等重大問題，監管機關、銀行與整個社會都必須共同面對。這些議題涵蓋技術、倫理、法律與政治等多重層面。
Algorithmic Bias and Fairness
AI systems in banking, particularly those used to help make credit decisions, may inadvertently discriminate against protected groups, with AI models that use alternative data like education or location potentially relying on proxies for protected characteristics, leading to disparate impact or treatment. This challenge emerges because AI systems learn patterns from historical data that may reflect past discrimination. If historical lending data shows that applicants from certain neighborhoods or with certain characteristics were denied credit, AI systems may learn to replicate these patterns even when the underlying factors don't represent legitimate credit risk indicators.
銀行業中的AI系統，尤其是在協助信貸決策的應用上，可能會不經意地對受保護族群產生歧視。AI模型若採用如教育程度、居住地等另類數據，這些資料項目或許間接反映了受保護特徵，進而產生差別待遇。這項挑戰的根源在於AI系統學習自過去的歷史數據，這些數據本身可能反映過往的歧視。如果歷史信貸資料顯示來自某些地區或具某些特徵的申請人較易被拒絕，AI系統即使無意，也可能複製這些模式，儘管這些特徵並不代表合法且合理的信貸風險指標。
The problem extends beyond simple replication of historical bias. AI can amplify bias through feedback loops where algorithmic decisions influence future data in ways that reinforce initial patterns. For example, if an AI system denies credit to members of a particular group, those individuals cannot build credit histories that might later demonstrate creditworthiness, perpetuating the cycle.
問題不僅止於單純複製歷史偏見。AI還可能經由演算法決策與新資料的交互強化偏見，形成反饋循環。例如，若AI系統經常拒絕特定族群的信貸申請，這些人將無機會建立良好信用紀錄，將來更難展現其信用價值，產生惡性循環。
Addressing algorithmic bias requires technical solutions, policy frameworks, and institutional commitments. Financial institutions must continuously monitor and audit AI models to ensure they do not produce biased outcomes, with transparency in decision-making processes crucial to avoiding disparate impacts. This monitoring must extend beyond simple outcome analysis to examine the factors AI systems use for decisions and ensure they don't rely on proxies for protected characteristics.
消除演算法偏見需結合技術解決方案、政策架構與機構承諾。金融機構必須持續監控及稽核AI模型，確保其決策結果無失公平，並要求決策過程具有透明性，以避免不同族群受到差別待遇。監測工作不應只著重於最終結果，更要檢視模型用來決策的所有數據因素，確保不存在以受保護特徵之代理指標進行判斷的情況。
The challenge intensifies as AI systems become more sophisticated. Simple models using limited variables can be audited straightforwardly - analysts can examine each factor and assess whether it represents legitimate business considerations or problematic proxies for protected characteristics. Complex neural networks processing hundreds of variables through multiple hidden layers resist such straightforward analysis. They may achieve better predictive accuracy but at the cost of reduced transparency.
隨著AI系統更加先進，這項挑戰日益嚴峻。以少量變數為基礎的簡單模型，其審查工作相對簡單——分析人員可以逐一檢視每個因素判斷是否合理、是否涉及受保護特徵的間接代理。然而，需處理數百變數、多層隱藏結構的複雜神經網路則難以進行類似分析。雖然這類模型預測能力更佳，卻以透明度降低為代價。
Data Privacy and Security
Banks hold vast amounts of sensitive personal information - financial transactions, account balances, investment positions, personal identifiers, behavioral patterns. AI systems require access to this data to function effectively, creating tension between AI's data appetite and privacy imperatives. The increasing volume of data and the use of non-traditional sources like social media profiles for credit decision-making raise significant concerns about how sensitive information is stored, accessed, and protected from breaches, with consumers not always aware of or consenting to the use of their data.
銀行掌握大量敏感個資，例如金融交易、帳戶餘額、投資部位、個人識別資訊及行為數據等。AI系統為了發揮最大效用，需讀取這些資料，這形成AI對資料的高度需求與資料隱私目標間的矛盾。例如愈來愈多的數據來源及社群媒體等非傳統資料作為信貸決策的參考，也引發消費資料的儲存、存取與防洩密保護等匱乏，且消費者往往未意識到或未同意其數據這樣被使用。
The privacy challenge extends beyond traditional data security to questions about data usage. Customers may consent to banks using their transaction data for fraud detection but not expect that same data to inform marketing algorithms or be shared with third parties. As AI systems become more sophisticated at extracting insights from data, the line between uses customers expect and approve versus those they find intrusive becomes increasingly important.
隱私挑戰不僅關乎資料安全，更延伸至個資用途議題。例如客戶可能同意銀行利用其交易資料偵測詐騙，卻未必接受同樣資料用於行銷推播或分享予第三方。愈來愈進階的AI數據挖掘功能，讓消費者可接受之用途與感到侵犯之用途之間的界線更形重要。
The technical challenge of privacy-preserving AI remains largely unsolved. Techniques like federated learning - where AI trains across distributed data without centralizing it - and differential privacy - where noise is added to data to protect individual privacy while preserving aggregate patterns - show promise but aren't yet mature enough for widespread banking deployment. Most AI systems still require access to detailed individual-level data to achieve optimal performance.
現階段保存隱私的AI技術仍未成熟。聯邦學習（讓AI能分散在多處資料上進行訓練而非集中存取），差分隱私（在資料中加入雜訊以保護個人隱私但保留整體數據趨勢）等新技術雖具發展潛力，卻尚不適合大規模銀行應用。目前大多數AI依然需要細緻的個人層級資料才能取得最佳表現。
Model Opacity and Explainability
The German regulator BaFin stated that the extent to which a black box could be acceptable in supervisory terms is dependent on how the model concerned is treated in the bank's risk management, with expectation that financial service providers can explain model outputs as well as identify and manage changes in AI models' performance and behavior. This regulatory perspective captures a fundamental tension in AI banking: the most powerful AI systems are often the least explainable.
德國監管機構BaFin表示，監管上能否接受黑箱模型，取決於銀行如何將該模型納入風險管理，並期望金融服務業者能解釋模型輸出結果，辨識及管理AI模型表現與行為的變異。這種監管觀點反映出銀行AI領域的根本矛盾：功能最強大的AI系統，通常也是最難解釋的。
Traditional credit scoring models used linear regression with a handful of variables, making it straightforward to explain why any particular applicant received a specific score. Modern AI systems may use ensemble methods combining multiple models, neural networks with hidden layers, or other approaches that resist simple explanation. A bank might be able to demonstrate statistically that such a system performs better than simpler alternatives but struggle to explain why it made any specific decision.
傳統的信用評分模型以線性回歸及少數變數為主，能很清楚地解釋為何某申請人會得到特定分數。現代AI系統則可能採用多模型集成、具多層隱藏層的神經網路等難以解釋的技術。銀行也許可用統計數據證明其預測準確性優於簡單模型，但卻難以確切解釋每個個別決策的原因。
This opacity creates problems for consumers who want to understand why they were denied credit or charged higher interest rates. It creates problems for regulators trying to assess whether models are fair and appropriate. It creates problems for banks trying to manage model risk and ensure their systems behave appropriately. The lack of explainability becomes particularly problematic when AI systems make consequential decisions that affect people's financial lives.
這種不透明性對消費者來說是一大問題：他們往往希望明白自己為何會被拒貸或被收取較高利率。對監管機關而言，這也妨礙其評估模型是否公正合理。對銀行而言，這則加大了模型風險管理的困難。尤其當AI系統所作決策直接影響民眾財務生活時，缺乏解釋性的問題便更加嚴峻。
Regulatory approaches to explainability vary. The SEC implements the Market Access Rule mandating strict pre-trade risk controls to prevent market manipulation and erroneous trading, and joint guidance from OCC, Federal Reserve, CFPB, and FTC highlights explainability, bias mitigation, and consumer transparency requirements. These frameworks establish principles for AI transparency but often lack specific technical requirements, leaving banks to determine how to satisfy regulators that their systems are appropriate.
各國對模型解釋性的監管作法不一。美國證券交易委員會(SEC)實施市場准入規則，要求嚴格的事前風險控管防止市場操縱與失誤交易；而美國貨幣監理署(OCC)、聯邦準備理事會、消費者金融保護局(CFPB)及聯邦貿易委員會(FTC)共同發布指南，強調解釋性、偏見減緩及消費者透明等要求。這些框架雖確立AI透明度的原則，但常缺乏明確的技術標準，導致銀行須自行決定如何向監管者證明其系統合規。
Systemic Risk and Stability
AI's impact on financial stability raises concerns that extend beyond individual institutions. If many banks deploy similar AI systems trained on similar data, their behavior may become correlated in ways that amplify market volatility or create systemic vulnerabilities. During market stress, AI trading systems might simultaneously try to sell the same assets or hedge the same risks, exacerbating price movements and potentially triggering cascading effects across financial markets.
AI對金融穩定性的影響，超越單一金融機構的層面。假如多數銀行導入類似的AI系統，並訓練於相同類型的資料，他們的行為可能表現出高度一致性，易加劇市場波動或產生系統性脆弱性。在市場壓力劇增時，AI交易系統可能同步拋售同類資產或對相同風險進行對沖，加劇價格劇烈波動，甚至引發連鎖性金融市場衝擊。
The complexity of AI systems also creates operational risks. Banks become dependent on AI for critical functions, and failures or malfunctions could disrupt operations in ways that affect customers, counterparties, and markets. The interconnection of financial institutions means that AI failures at one bank could propagate through the financial system.
AI系統的複雜性也帶來營運風險。銀行若過度仰賴AI處理關鍵業務，任何錯誤或失靈都可能干擾營運並影響到客戶、交易對手及整個市場。金融機構的高度互聯性，也意味著一間銀行AI系統失效可能蔓延至整個體系。
Citi projects 10 percent of global market turnover to be conducted through tokenized assets by 2030, with bank-issued stablecoins as the main enabler, and 86 percent of surveyed firms piloting generative AI for client onboarding and post-trade specifically. The convergence of AI and tokenization creates new systemic risk considerations as financial assets migrate to blockchain-based infrastructure where AI agents could execute transactions autonomously.
花旗銀行預估，到2030年全球10%的市場交易額將透過代幣化資產完成，其中由銀行發行的穩定幣將扮演關鍵角色；調查顯示，有86%企業正試行生成式AI於客戶開戶與交易後流程。隨著金融資產轉向區塊鏈基礎建設，AI與代幣化的融合帶來全新系統性風險，因AI代理人可在無人監管下自動執行交易。
Accountability and Liability
When AI systems make decisions that result in harm - discriminatory lending, erroneous trading, privacy violations - questions of accountability become complex. Traditional liability frameworks assume human decision-makers who can be held responsible for choices. AI distributes decision-making across human-machine systems in ways that obscure responsibility.
當AI系統的決策造成損害——例如歧視性貸款、錯誤交易或侵害隱私時，責任歸屬變得相當複雜。傳統的法律責任架構假設由人類決策並可區分責任。AI則將決策分散在人機系統間，使責任變得模糊不清。
If an AI-powered lending system systematically discriminates against a protected class, who bears liability? The data scientists who built the model? The business managers who deployed it? The executives who approved the AI strategy? The bank as an institution? These questions lack clear answers under current legal frameworks, creating uncertainty for both banks and consumers.
如果AI信貸系統不斷對某受保護族群產生歧視責任該由誰承擔？是設計模型的數據科學家？推動上線的業務經理？批准AI策略的高層？還是整間銀行？現行法規並無明確答案，這對銀行與消費者都帶來不確定性。
Regulatory Landscape
The EU AI Act, effective by mid-2025, classifies AI systems by risk, with high-risk applications in finance like credit assessments and insurance pricing requiring transparency, human oversight, and bias mitigation, with financial firms required to document and justify AI decisions, setting a global standard for responsible AI. The European approach establishes comprehensive regulatory frameworks specifically addressing AI risks.
歐盟AI法案預計在2025年年中生效，依風險等級劃分AI應用，其中金融領域如信貸評估與保險定價等高風險應用，需符合高度透明、人類監督與偏見抑制要求，並要求金融機構有義務記錄與說明AI決策的依據，堪稱負責任AI的全球標竿。歐洲此舉建立了針對AI風險的完整監管架構。
American regulation, by contrast, remains fragmented. President Trump signed Executive Order 14179 on January 23, 2025, revoking President Biden's comprehensive AI
相比之下，美國的AI監管仍相當零散。川普總統於2025年1月23日簽署第14179號行政命令，撤銷了拜登總統先前提出的整體AI──（請依據您的要求，「跳過 markdown 連結的翻譯」。若段落內有 markdown link，該連結部分維持英文不變。）
Executive Order, with the Trump administration moving to deregulate AI use. This created regulatory uncertainty as federal frameworks were rolled back, leaving state regulators stepping in, passing legislation focused on bias, transparency, and compliance in AI-driven decision-making for lending and employment, with several states clarifying that discriminatory AI behavior would be assessed under their Unfair or Deceptive Acts or Practices laws, creating a patchwork of oversight.
川普政府推動放寬對人工智慧應用的監管，發布行政命令。這導致聯邦監管框架被削弱，產生監管上的不確定性，州級監管機構因此介入，通過聚焦偏見、透明度與合規性的相關法規，針對以 AI 為核心的放貸與僱用決策進行管理。多個州明確表示，歧視性的 AI 行為將根據其「不公平或欺騙性行為或做法法」（Unfair or Deceptive Acts or Practices laws）來評估，造成監管架構呈現拼貼式的狀況。
The National Credit Union Administration lacks model risk management guidance with sufficient detail on how credit unions should manage model risks, including AI models, and the authority to examine technology service providers despite credit unions' increasing reliance on them for AI-driven services. This regulatory gap illustrates the challenge that AI outpaces regulatory capacity, with institutions deploying sophisticated systems faster than oversight frameworks can adapt.
美國國家信用合作社管理局（NCUA）缺乏足夠詳細的模型風險管理指引，說明信用合作社應如何管理包括 AI 模型在內的模型風險，也沒有充分權限檢查科技服務供應商，儘管信用合作社對這些供應商在 AI 服務上的依賴持續增加。這項監管落差顯示，AI 的發展速度已超越監管單位的因應能力，機構部署先進系統的速度遠快於監管架構的適應速度。
Regulatory agencies should require banks to indicate whether they use AI to comply with Community Reinvestment Act regulations, require those systems to be explainable, require third-party AI audits for all institutions, and require banks to periodically review their Bank Secrecy Act systems to ensure accuracy and explainability. These proposals reflect growing recognition that AI in banking requires new forms of oversight, but translating principles into enforceable requirements remains a work in progress.
監管機關應要求銀行說明是否使用 AI 來遵循《社區再投資法》（Community Reinvestment Act）的規定，並要求這些系統具有可解釋性，所有機構必須進行第三方 AI 審查，同時定期檢視其《銀行保密法》（Bank Secrecy Act）系統以確保準確性與可解釋性。這些建議反映對銀行業 AI 風險監管需求的日益重視，但如何將原則轉化為可執行的要求，仍在發展當中。
The global dimension complicates regulatory development. Banks operate across multiple jurisdictions with different regulatory approaches to AI. Institutions must navigate the EU AI Act, various national frameworks in Asia, state-level requirements in the United States, and emerging standards from international bodies like the Bank for International Settlements. This regulatory fragmentation creates compliance complexity and may slow AI deployment in cross-border banking operations.
全球性因素使監管發展變得更加複雜。銀行在多個司法轄區內運作，各自對 AI 有不同的監管方法。機構必須應對歐盟 AI 法案、亞洲各國不同的國家政策、美國各州的要求，以及國際清算銀行等國際組織提出的新興標準。這種監管破碎化提高了法規遵循的複雜性，也可能拖慢跨境銀行業務導入 AI 的步伐。
AI Banking Versus Autonomous Finance: The DeFi Comparison
AI 驅動的傳統銀行與去中心化金融（DeFi）的興起恰逢其時，形成兩種不同技術引領的金融轉型路徑間的有趣對比。AI 銀行透過智能與自動化強化傳統機構，而 DeFi 則利用區塊鏈協議，實現無需傳統中介的金融服務。這兩種方式的融合與競爭正逐步塑造金融的未來走向。
Stablecoins and Tokenization
穩定幣流通量在過去 18 個月中倍增，但每日僅促成約 300 億美元的交易，僅佔全球資金流動量不到百分之一。倡議者認為該技術可超越銀行工作時間與國界，提升現有支付基礎設施的速度、成本、透明度、可及性，並讓更多未被銀行服務的人口得以納入。這些數位資產以區塊鏈為基礎，等同現金等價物，使 24 小時全年無休結算成為可能，無需傳統銀行中介參與。
代幣化預計到 2030 年將有高達 16 兆美元的實體資產上鏈，改變全球金融運作方式，華爾街龍頭如 BlackRock、JPMorgan、Goldman Sachs 等已推動代幣化債券、國庫券與存款的試點計畫。這顯示傳統金融機構越來越將區塊鏈基礎設施視為輔助而非競爭威脅。
當機構利用 AI 來管理代幣化資產時，AI 銀行與代幣化的關聯性更加凸顯。花旗預計到 2030 年將有 10% 的全球市場交易額實現代幣化，銀行發行穩定幣有助於提升抵押效率與基金代幣化。調查顯示 86% 的金融機構正在測試 AI 用於客戶開戶，這是資產管理、託管與經紀交易的關鍵場景。這種融合預示，未來的 AI 系統將橫跨傳統銀行架構與區塊鏈的代幣化資產。
Autonomous Protocols Versus AI Agents
DeFi 協議透過智能合約執行金融交易－智能合約是部署於區塊鏈的程式碼，能根據預先設定的規則自動執行交易。這些協議涵蓋借貸、交易、衍生品等多項金融功能，無需人工中介。願景是在去中心化網路上以軟體運作金融服務，而非機構層級的作業。
AI 代理人則在銀行內部發揮類似作用，但其運作仍受機構框架限制。AI 並非取代銀行，而是提升銀行效能與能力。兩者根本差異在於治理與控制。DeFi 協議部署後將依其程式碼自動運作，治理往往分布於代幣持有者之間；AI 代理人則受制於銀行機構，銀行保有其行為的控制權與責任。
這造成不同的風險與報酬結構。DeFi 提供抗審查、全年無休、程式碼透明、對傳統中介依賴度低等優點，但也涉及智能合約風險、發生問題時的求償有限、監管不確定及難以大規模普及等挑戰。AI 驅動的傳統銀行則具備法規遵循、消費保護、完備爭端解決機制以及可與傳統金融基礎設施整合的優勢，但仍維持把關角色、制度約束與可能高於 DeFi 的成本結構。
Regulatory Treatment
全球多項法規著眼於確保代幣化現金運作的穩定與安全，規範其準備金、資訊披露、洗錢防制與 KYC（了解你的客戶）以及相關執照。以美國 2025 年 6 月跨越參院通過的“美國穩定幣國家創新法案”（Guiding and Establishing National Innovation for U.S. Stablecoins Act of 2025）為例，明定準備金、穩定性及監管要求。這些法規說明穩定幣及代幣化正逐步走出法規灰色地帶，形成較為明確的監理架構。
聯邦準備理事會舉辦會議專注於支付創新，討論穩定幣、去中心化金融、人工智慧和代幣化等議題。聯準會理事 Christopher Waller 表示，這些科技可優化支付作業並強化私部門協作。此舉顯示央行認識到這些技術的潛在影響，並積極研究其與貨幣政策及金融穩定的交集。
這種監管動態帶來銀行的重要策略抉擇：他們應僅在傳統金融基礎上發展 AI 能力，或同時建立能跨足區塊鏈 DeFi 協議的 AI 應用？他們應自行發行穩定幣與其他發行方競爭，或將現有穩定幣納入營運？該如何兼顧區塊鏈結算的效率優勢與監管複雜性、技術風險？
Hybrid Architectures
最有可能的發展是結合傳統銀行、AI 能力與區塊鏈基礎設施的混合式路線。銀行可發行以傳統準備金支撐的代幣化存款或穩定幣，實現基於區塊鏈的結算同時提供機構擔保。AI 系統可跨傳統支付管道及區塊鏈網路運作，根據成本、速度等因素最佳化路由。
Consensus 2025 的討論強調去中心化金融的急速發展，聚焦於去中心化交易所的普及、穩定幣使用激增、實體資產代幣化興起、收益協議成長等議題，並出現在法規逐漸明朗的背景下。這顯示傳統金融與 DeFi 間的邊界正變得越來越模糊。
AI 與區塊鏈的結合帶來了新的技術可能性。智能合約可以導入 AI 決策機制，讓協議能因應市場變化自動調整行為。AI 可監控區塊鏈交易防範詐欺、分析 DeFi 協議健康狀況、優化跨協議的收益策略。銀行甚至可部署可同時操作於傳統金融基礎設施與 DeFi 協議的 AI 代理人，為客戶帶來一體化服務。
這種融合也引發對金融仲介未來的哲學省思。如果 AI 能自動化大多數銀行功能，而區塊鏈有能力在無需傳統中介的情況下完成交易，我們是否還需要銀行這樣的機構？還是金融將演化成 AI 代理人在去中心化協議中代表用戶操作，而傳統銀行要不是擁抱這新架構就是逐漸失去關鍵性？
答案很大程度上取決於監管演變、消費者偏好與技術成熟度。如果監管架構能順利引入區塊鏈金融服務並保護消費者，混合模式或將成為主流；若區塊鏈規模化遇阻或監管傾向維護傳統機構，AI 銀行可能維持主導地位。最可能的結果，是傳統銀行、AI 強化機構與去中心化協議並存，滿足不同需求與偏好。
The True AI Bank: A 2030 Vision
（以目前趨勢延伸推估...） toward their logical conclusion allows us to envision what a genuine AI bank might look like when the transformation reaches maturity, likely sometime in the early 2030s. This vision helps clarify what fundamental transformation means and raises profound questions about whether such an institution still represents a "bank" in any traditional sense.
朝著其邏輯終點發展，使我們能夠想像當這場轉型於2030年代初期達到成熟時，一家真正的AI銀行會是什麼模樣。這個願景有助於闡明「根本性轉型」的意義，同時引發深刻的疑問：如此機構是否仍然屬於傳統意義上的「銀行」。
Universal AI Assistance
In a true AI bank, every employee operates with a personal AI assistant deeply integrated into all workflows. Investment bankers instruct their AI to prepare client meeting materials, analyze potential acquisition targets, or draft term sheets. Traders direct AI agents to monitor markets, execute strategies, and optimize portfolios. Compliance officers task AI with monitoring transactions for suspicious patterns, generating regulatory reports, and researching regulatory changes. Technology teams use AI for software development, infrastructure management, and system optimization.
在真正的AI銀行裡，每位員工都配備有深度整合於全部工作流程中的個人AI助理。投資銀行家會指示他們的AI準備客戶會議資料、分析潛在收購目標，或撰寫條款清單。交易員讓AI代理人監控市場、執行策略並優化投資組合。合規人員交託AI監控可疑交易模式、產出監管報告並研究法規變更。科技團隊則利用AI開發軟體、管理基礎設施以及進行系統優化。
These AI assistants don't simply respond to individual queries like current chatbots. They maintain context across conversations, proactively identify tasks that need completion, schedule their own meetings with other AI assistants to coordinate work, and continuously learn from interactions to better anticipate needs. The human role shifts toward setting strategic direction, making high-level decisions, and handling situations requiring judgment, creativity, or interpersonal skills that AI lacks.
這些AI助理並不像現有聊天機器人只被動回應提問。它們能夠維持對話脈絡、主動辨識待完成的任務，甚至可自行與其他AI助理安排會議協調工作；並透過不斷學習互動經驗，更好地預測需求。人類角色轉為設定策略方向、做出高層決策，以及處理那些需要判斷力、創造力或人際溝通技巧——是AI難以勝任的情境。
Autonomous Operational Processes
Core banking operations - account opening, payment processing, trade settlement, reconciliation, regulatory reporting - flow through AI systems with minimal human intervention. These systems don't follow rigid scripts but adapt behavior based on context. They detect anomalies and determine whether to flag them for human review or resolve them autonomously. They optimize resource allocation dynamically rather than following static rules. They identify process improvements and implement changes after appropriate approval.
銀行核心營運流程，如開戶、付款處理、交易結算、對帳及監管報告，都在AI系統下自動化進行且幾乎無需人為干預。這些系統不再遵循僵化的流程，而是根據情境靈活調整行為。它們能偵測異常，並決定該交由人類審核還是自行處理；也能動態優化資源分配，而非僅遵守固定規則。此外，它們會主動找出流程改進點並經適當審批後實施。
The traditional operations workforce largely disappears, replaced by smaller teams of engineers, analysts, and oversight specialists who monitor AI systems, handle edge cases, and continuously refine automated processes. The efficiency gains prove dramatic - processes that required thousands of employees complete with dozens, and processing times measured in days compress to seconds.
傳統營運團隊大致消失，改由少數工程師、分析師和監控專家組成的小組負責監督AI系統、處理特殊案例並持續優化自動化流程。效率提升極為驚人——過去需數千人參與的作業，現只需幾十人即可完成；以天計的處理時間也縮短至幾秒內完成。
AI-Curated Customer Experiences
Every customer interaction - whether through mobile apps, websites, phone calls, or in-person branches - flows through AI that personalizes the experience based on comprehensive understanding of the customer's financial situation, preferences, goals, and behavioral patterns. The AI doesn't offer generic products but instead designs solutions tailored to individual circumstances.
每一次的客戶互動——不論是透過行動App、網站、電話還是實體分行——都經由AI處理，根據對顧客財務狀況、偏好、目標與行為模式的全盤理解，打造個人化體驗。AI不再推銷制式產品，而是根據個別情況設計專屬解決方案。
For retail customers, AI provides financial planning guidance that rivals human advisors, monitors spending patterns to identify savings opportunities, and proactively suggests actions to improve financial health. It detects life events - a new job, a home purchase, a child's birth - and adjusts recommendations accordingly. For corporate clients, AI analyzes business operations, identifies financial optimization opportunities, and structures tailored banking solutions.
對零售客戶來說，AI能提供媲美真人理財顧問的建議，追蹤消費模式以找出儲蓄機會，並主動提出改善財務健康的行動建議。AI也會偵測人生重要事件，比如轉職、購屋、添丁，並隨之調整推薦內容。對企業客戶而言，AI會分析業務營運，找出財務優化的機會並設計量身訂做的銀行方案。
The human advisor role doesn't disappear but evolves. For high-net-worth individuals and complex corporate clients, humans provide strategic counsel, relationship management, and judgment on sophisticated financial decisions. For routine needs and standard products, AI handles interactions entirely.
人類顧問的角色並未消失，而是進化了。對高資產客戶及複雜企業用戶，仍需人類進行策略諮詢、關係維護與精密財務決策的判斷。至於日常需求與標準產品，則完全由AI處理互動。
Intelligent Risk Management
Risk management becomes continuous, comprehensive, and adaptive rather than periodic and rules-based. AI systems monitor every transaction, every position, every counterparty exposure in real-time. They detect subtle patterns indicating emerging risks before they manifest as losses. They conduct scenario analysis across hundreds of potential futures, identifying vulnerabilities and suggesting mitigations. They optimize capital allocation to maximize risk-adjusted returns while maintaining regulatory compliance.
風險管理不再是定期、規則本位的流程，而轉為連續、全面且能自我調整。AI系統可即時監控每筆交易、每項部位、每個對手方曝險。它們可偵測微妙新興風險的徵兆，還未造成損失前就著手因應。藉著分析數百種未來情境，主動找出脆弱點並提出緩解建議。AI並能優化資本分配以實現風險調整後的最大報酬，同時確保合規。
Credit decisions happen instantaneously through AI analysis that considers far more factors than traditional underwriting - transaction patterns, behavioral signals, external data sources, and subtle correlations that human analysts would never detect. The result is both more accurate risk assessment and greater financial inclusion as AI can extend credit to customers who lack traditional credit histories but demonstrate creditworthiness through alternative indicators.
信貸決策經AI即時分析而成，考慮的要素遠多於傳統核貸流程——包括交易模式、行為訊號、外部數據來源及人類無法察覺的微妙相關性。最終，不僅風險評估更準確，也推動金融普惠，使無傳統信用紀錄但具信用跡象的客戶也獲得授信。
Agentic Trading and Treasury Management
Trading evolves from humans making decisions with AI assistance to AI agents executing strategies under human supervision. These agents don't simply follow instructions but adapt tactics dynamically based on market conditions. They identify opportunities, assess risks, and execute trades across multiple markets and asset classes simultaneously.
交易將從人類依靠AI協助決策，進化為AI代理人主導策略、由人類監督。這些代理人不僅執行指令，而是能根據市場狀況動態調整戰術，發掘投資機會、評估風險，並於多個市場和資產類別同時執行交易。
Treasury operations become largely autonomous, with AI managing liquidity, optimizing funding costs, deploying capital efficiently, and managing regulatory capital requirements. The systems continuously learn from outcomes and refine their strategies, achieving performance that surpasses human traders while operating at scale impossible for human teams.
資金調度作業也高度自動化，由AI管理流動性、優化資金成本、高效配置資本並處理法定資本需求。這些系統不斷從結果中學習優化策略，達成超越人類交易員的表現，並能以人類團隊難以達成的規模運作。
Seamless Cross-Border Operations
The AI bank operates globally as a unified institution rather than as a collection of regional operations. AI systems handle cross-border transactions, navigate different regulatory regimes, manage multiple currencies, and optimize global operations. Language barriers disappear as AI provides real-time translation. Time zone differences become irrelevant as AI operates 24/7. Regulatory complexity gets managed through AI that tracks requirements across jurisdictions and ensures compliance.
AI銀行作為一個統一整合的全球機構運作，而非一系列區域單位。AI系統處理跨境交易、應對多樣監管體系、管理多幣別並優化全球營運。語言障礙不再存在，因AI可即時翻譯；時區也不再重要，因為AI全年無休運作。監管複雜度則交由追蹤各地要求並確保合規的AI管理。
Predictive and Proactive Banking
Rather than reacting to customer requests, the AI bank anticipates needs. It identifies when a customer will likely need credit and offers it proactively. It detects when a business client might face cash flow challenges and suggests solutions before crises emerge. It recognizes market conditions where clients might benefit from portfolio adjustments and recommends actions.
AI銀行不再只是被動回應客戶需求，而是預先洞察並主動服務。它能預測客戶何時需要信貸並主動提供；偵測企業客戶可能會有現金流困難，在危機爆發前提出解決方案；也能辨識有利於客戶調整資產組合的市場情況並提出建議。
This proactive approach extends to risk management, where AI predicts potential fraud before it occurs, identifies emerging cyber threats, and detects operational vulnerabilities. The institution shifts from managing problems to preventing them.
這種主動模式也應用於風險管理層面，AI能預先偵測未發生的詐騙、找出新興資安威脅、發現營運漏洞。機構的角色從「處理問題」轉化為「預防問題」。
Organizational Structure
The organizational structure of a true AI bank differs dramatically from traditional banks. The massive hierarchical structures of traditional banking - layers of management overseeing armies of workers performing specialized functions - give way to flatter organizations where smaller teams of specialized experts oversee AI systems executing work.
真正AI銀行的組織結構與傳統銀行大相逕庭。傳統銀行擁有層層管理、無數分工人力的金字塔型架構，如今轉型為由少數專家小組監控AI完成各項工作的扁平化組織。
Job categories shift from operators to orchestrators, from executors to strategists, from processors to problem-solvers. The institution becomes a hybrid human-AI organization where defining the boundary between human and machine contributions becomes difficult.
職務類型從執行者轉變為統籌者、從執行手轉變為策略規劃者，從流程處理者轉型為問題解決專家。這家機構成為人機混編團隊，人與AI間的職責界線逐漸模糊。
The Category Question
This raises a profound question: Is such an institution still a "bank," or does it represent something fundamentally new - an intelligent financial system that happens to be organized as a corporation? Traditional banks are human organizations that provide financial services. AI banks are artificial intelligence systems governed by humans that provide financial services. The distinction may seem semantic, but it carries implications for regulation, liability, corporate governance, and how we think about financial institutions' role in society.
由此帶來一個深刻問題：這樣的機構還能稱為「銀行」嗎？還是它本質上已是全新物種——只是以公司型態存在的智慧型金融系統？傳統銀行是由人組成、提供金融服務的組織；AI銀行則是由人類治理的人工智慧系統，來提供金融服務。這種區別看似語義遊戲，實則深刻影響監管、責任歸屬、公司治理、以及我們對金融機構社會角色的思考。
If banking work largely flows through AI systems, with humans providing oversight and strategic direction but not executing most tasks, how should we regulate such institutions? Do traditional frameworks built around human decision-making and accountability still apply? What happens when AI systems make decisions that harm customers or create systemic risks?
若銀行大部分作業都由AI主導，人類只負責監督與戰略規劃，那我們該如何監管此類機構？現有圍繞人類決策與責任設計的監管架構，是否還合用？當AI做出傷害客戶或產生系統性風險的決策時，責任如何歸屬？
These questions lack clear answers, and grappling with them will occupy regulators, legal scholars, ethicists, and industry participants throughout the coming decade. The transformation of banking through AI represents not just technological change but institutional evolution that challenges foundational assumptions about how financial services should be organized and governed.
這些問題目前無法明確解答，未來十年中，監管單位、法學家、倫理專家及業界都將不得不面對這些挑戰。AI驅動下的銀行轉型，不僅是技術革命，更是金融體制根本假設的變革。
Final thoughts
The transformation of banking through artificial intelligence has moved from speculative possibility to operational reality. JPMorgan Chase is being "fundamentally rewired" for the AI era, with plans to provide every employee with AI agents, automate every behind-the-scenes process, and curate every client experience with AI. This vision, while ambitious, increasingly appears achievable rather than fantastical.
人工智慧帶來的銀行業變革已從假想推測變爲現實運作。摩根大通正在為AI時代「根本性重構」，計畫讓每位員工配備AI代理人、每項幕後流程自動化、每一次客戶體驗皆由AI精心打造。這個願景雖然雄心勃勃，但已漸漸從天馬行空轉為可實現。
The drivers of this transformation prove powerful and mutually reinforcing. Competitive dynamics compel banks to deploy AI or risk being outcompeted by institutions that do. Technological capabilities continue advancing at remarkable pace, with AI systems demonstrating competence across tasks previously thought to require uniquely human intelligence. Economic pressures favor automation that reduces costs while improving service quality. Customer expectations evolve toward digital experiences that demand sophistication only AI can deliver at scale.
這場轉型背後的動力強大且彼此強化。競爭壓力迫使銀行導入AI，否則將落後於先行者；技術能力持續高速進步，AI已能勝任過去只有人類才能處理的複雜任務。經濟效益推動自動化，既降低成本又提升服務品質。客戶期待則逐漸向數位化、高度智慧且大規模僅AI才能提供的體驗靠攏。
The implications extend far beyond banking efficiency. This transformation will reshape employment across the industry, with Bloomberg Intelligence finding that global banks are expected to cut as many as 200,000 jobs in the next three to five years as AI takes on more tasks. It will concentrate economic advantages among institutions that successfully deploy AI while potentially marginalizing those that lag. It will raise profound questions
這些影響遠不止銀行效率提升。此一變革將重塑產業就業結構——據彭博資訊預估，未來三到五年，全球銀行有望因AI自動化而裁撤多達20萬個職位。成功部署AI的機構將獲得更大經濟利潤，而落後者則可能被邊緣化。這還將引發一系列深刻問題以下為翻譯內容（已依指示保留 markdown 連結未翻譯）：
有關演算法公平性、問責制，以及人在金融決策中扮演的角色。
監管上的挑戰極為艱鉅。歐盟《人工智慧法案》以風險分類AI系統，並要求在高風險金融應用中實施透明化、人為監督及偏誤抑制，從而制定了全球標準。然而，在大多數司法管轄區內，全面性的規範框架仍在發展階段，而科技變革的速度遠超過監管調適的步伐。這使得投入巨額資金發展AI能力的金融機構，難以明確了解未來的監管要求，增添了諸多不確定性。
AI銀行與區塊鏈金融的融合，將這場轉型提升到另一層次。預估到2030年，資產代幣化將使高達16兆美元的實體資產上鏈，主要銀行已開始試行代幣化債券和存款。AI、傳統銀行體系與去中心化協議的交會，有可能催生出混合式架構，結合自動化的效率、區塊鏈的透明度，以及受監管機構的穩定性。
AI驅動的銀行是否無可避免，端看如何定義「AI驅動」。可以確定的是，每一家大型銀行都將部署大量AI能力——競爭壓力促使其發展。但銀行是否會成為JPMorgan所描繪的那種全方位AI連結企業則仍未可知，還需成功跨越技術挑戰、監管演進，以及組織變革管理等關卡。
最明確的是，2030年的銀行業將與今日大不相同。經過這場轉型後，這些機構也許只在表面上與過去相似，本質上則已圍繞人工智慧徹底重塑。是稱這些機構為「AI銀行」、「智能金融機構」，或僅僅稱為「銀行」，其實都不那麼重要；更重要的是，我們已站在一個關鍵的轉折點上，科技正根本性地重定義銀行的意義，以及金融服務的運作方式。
這場轉型同時帶來風險與機遇。它可能讓大型機構的優勢進一步固化，若AI系統延續偏見則可能加劇金融排除，也可能產生新的系統性風險，並使成千上萬的勞工被取代。如何在享受AI帶來的利益同時，有效管理這些風險，正是產業、監管機關與決策者的核心挑戰。
最終的問題，也許是AI驅動的銀行是否會比傳統機構更好地服務客戶與社會。如果AI能帶來更易獲取的金融服務、更公平的信貸決策、更佳的風險管理，以及營運效率提升，進而讓成本降低、顧客體驗更佳，那麼即使過程中出現衝擊，這場轉型仍值得推動。但若AI導致權力高度集中、偏見加劇、問責性降低，而主要利益只服務股東卻犧牲更廣泛的利害關係人，那就該引起謹慎。
答案將不取決於科技本身，而是要看機構與監管單位如何選擇部署及規管AI於銀行業。科技帶來轉型可能，但這場轉型是服務廣泛社會利益還是狹隘私人利益，仍取決於人的選擇。在面臨這個轉折點的時刻，這些選擇將形塑未來數十年的金融業。
真正的AI驅動銀行即將來臨。問題在於它會成為什麼樣的機構，以及會服務誰的利益。對這個問題做出審慎的回答，將決定這場轉型究竟帶來的是進步，還僅僅是改變。