去中心化金融發展至今已到十字路口。隨著數十億美元鎖定於借貸協議中，信貸市場迅猛擴張，整個生態正面臨嚴峻挑戰：在無需許可的環境下，如何準確評估與定價風險。DeFi 雖成功消除了傳統金融的把關者，但同時帶來資訊不透明的新問題。借貸者、放貸人與協議彼此對信用狀況了解有限，導致資本配置效率低下，也限制產業的增長潛力。
於是鏈上信貸評級應運而生——這是一個新興卻日益壯大的基礎設施層，旨在為去中心化市場引進透明、數據驅動的風險評估。與傳統金融各大評級公司（如 S&P、Moody's）主導信用評估不同，DeFi 的評分現狀呈現多元並進格局：從算法評分模型、風險預言機，到共識型評級協議及機構級評估平台，應有盡有。
像是 Gauntlet、Chaos Labs 以及 Credora 等公司，正分別打造各自對信用風險量化、分發與智能合約整合的願景。
這一變革之所以重要，在於 DeFi 目前 1270 億美元的鎖倉總值極度依賴超額抵押借貸——這種資本效率低落的模式限制了可及性與規模擴張。信貸評級為更精細化、風險導向的借貸鋪路。擁有優良鏈上信譽的用戶，可取得更高貸款成數，協議也能最佳化風險與收益結構，機構資本則可更有信心進入市場。
此外，鏈上信貸分數的標準化，還可能進一步實現 DeFi 與傳統金融的串接，催生代幣債務、實體資產借貸、跨境信用市場等嶄新承銷模式。
下文將介紹鏈上信貸評級機制，分析主流平台的建構方式，探討真實應用案例，並反思算法風險評估內在的風險與限制。隨著 DeFi 市場日趨成熟，信貸評級預期將如今日價格預言機一樣成為基礎設施，但前路仍需解決數據品質、模型透明度與監管不確定性等複雜挑戰。
什麼是鏈上信貸評級？
傳統金融長年依賴信貸評級來衡量借款人違約概率。企業發債或個人申請房貸時，評級機構會根據還款記錄、未償債務、營收穩定性等因素，評估其信用狀況，最終給出標準化分數或等級，例如最安全的 AAA 級，以至投機級或違約區。這些評級決定借貸條件及成本。
DeFi 一直缺乏這類架構。現有大多數借貸協議只用一個粗糙的工具：「超額抵押」。即借款人必須質押遠高於借款金額的資產，通常需達到 150% 或更高。若抵押品價值跌破門檻，協議便會自動清算，以保護放貸人。一方面這套機制有效避免損失，但極度資本效率不彰。即便是鏈上記錄完美的借貸者，也和新手或有多次清算紀錄的用戶承受同樣的抵押要求。
鏈上信貸評級則試圖為這種二元系統注入更多細膩度。其原理是分析借款人在區塊鏈上的各項歷史數據——交易行為、借貸習慣、清算事件、資產狀況、協議互動紀錄——據此生成定量風險分數。有些系統會給出數值評分（如 0-1000 分），另有一些則對應傳統等級（如 AAA 至 CCC），或直接量化違約機率。
其中的核心創新在於，這些分數可直接在鏈上調用、嵌入智能合約，動態調整借貸參數。例如高評級用戶可獲得 80% 的貸款成數，而低評級錢包僅能取得 60%；利率、清算門檻、借款上限皆可依信用動態調整，提高資本市場效率，獎勵良好行為，懲處高風險用戶。
近年學界亦開始系統化這些概念。2024 年有論文 《On-Chain Credit Risk Score in Decentralized Finance》 由 Ghosh 等人提出 OCCR 分數，這是一個針對錢包信用風險的機率化框架。OCCR 不倚賴經驗法則，而用統計學方法，根據歷史鏈上活動及預測場景，衡算違約機率。研究證實，DeFi 協議可根據風險評分即時調整貸款成數與清算門檻。
實際應用例如：想像某 DeFi 借貸池允許多種抵押品。傳統上協議會對所有用戶統一設定 70% LTV（貸款抵押成數）。而導入信貸評級後，協議可對信用佳的錢包（從未遭清算、有穩定還款、資產多元）提高至 75% LTV，對新用戶或風險較高錢包降至 65%。這將提升借款人的資本效率，同時給放貸人保留安全緩衝。
從過去的無需許可超額抵押借貸，轉向有評分的風險導向借貸，是 DeFi 結構的根本演進。這雖不會讓抵押品門檻消失（許多應用仍需抵押），但賦予更細緻的風險控制，也為高信譽用戶鋪設欠/零抵押借貸之路。
主流平台如何建立信貸評分模型
有三家公司在鏈上信貸評級基礎設施領域表現突出，各自採用不同的評級方法論，反映出對風險測量與佈署模式的不同哲學。
Gauntlet：模擬驅動的風險評分
Gauntlet 在 2020 年與 DeFi Pulse 合作，推出其 Economic Safety Grade 平台，率先將風險評分導入 DeFi。該公司以代理人建模與蒙地卡羅模擬為特色，針對極端市場條件對協議進行壓力測試。
Gauntlet 的風險分數主要評估借貸協議本身，而非單一用戶，重點關注系統性違約風險。平台會分析抵押品波動性、流動性、用戶行為、協議參數及清算人效率。通過大量模擬價格波動及清算場景，推估協議可能無法全額償付存款人的概率。
評分範圍為 1 至 100，Aave、Compound 初次評分都在 90 分以上。該模型也會找出每個協議「風險最高抵押品」（通常是波動或規模最大的頭寸），模擬違約情境：例如價格瞬間下跌 30%，到底有多少頭寸會觸發清算？清算人反應速度如何？多項資產同時暴跌又會怎樣？
除了協議級評級外，Gauntlet 還進一步拓展 機構級風險管理服務。公司現已營運多款針對機構資金的風險優化金庫，利用自身模擬平台動態調整各種 DeFi 配置。這些金庫即為信貸評分的實際應用：基於實時分析，將資本分配給風險回報比佳的協議。
Gauntlet 尤重數據嚴謹性與歷史回測。其模型曾準確預警 2020 年 3 月「黑色星期四」大規模清算風險，並幫助協議微調參數，以預防未來連鎖崩潰。著重於系統風險而非單一錢包評分，是 Gauntlet 的一大特色，公司將 DeFi 信貸評級視為協議設計與治理的重要工具。
Chaos Labs：即時風險預言機
Chaos Labs 則採取不同策略，致力構建所謂「風險預言機」——這是一種可向智能合約直接提供即時風險數據的基礎設施，實現參數的自動化調整。公司成立於 2021 年，獲 Haun Ventures、PayPal Ventures 等公司 5,500 萬美元投資，現已躋身主流協議的運營風險管理層。
其 Edge Risk Oracle 平台已於 2024 年底被 Aave 採用，可自動管理多鏈數千個風險參數。以往，協議調整清算門檻或上限需通過治理提案並有數天延遲，有了 Chaos Labs 預言機，則可根據市場情況即時動態調整。
實際運作方式如下：平台持續監控各借貸市場的抵押品流動性、波動劇烈程度、使用率。一旦觸發預設門檻，例如穩定幣脫鉤或流動性急遽下滑，預言機會自動於「合理範圍」內修正風險參數。 bounds" pre-approved by governance. During the March 2023 USDC depeg following Silicon Valley Bank's collapse, such automation could have paused new deposits, tightened liquidation thresholds, or implemented circuit breakers to prevent cascading losses.
由治理預先核准的「界限」。在 2023 年 3 月矽谷銀行倒閉引發 USDC 脫鉤期間，這類自動化機制本可暫停新存款、收緊清算門檻，或啟用熔斷機制來防範連鎖損失。
Chaos Labs' methodology combines on-chain data analysis with off-chain market intelligence. The platform processes data from centralized exchanges, blockchain transactions, liquidation events, and protocol analytics to build comprehensive risk profiles. Unlike Gauntlet's simulation-heavy approach, Chaos emphasizes real-time observability and rapid response.
Chaos Labs 的方法結合了鏈上數據分析與鏈下市場情報。該平台彙整來自中心化交易所、區塊鏈交易、清算事件與協議數據的資訊，建立全方位的風險檔案。不同於 Gauntlet 偏重模擬的方式，Chaos 強調即時可觀察性與快速回應能力。
The company now serves Aave's $19 billion in total value locked across 10+ networks, each with dozens of markets and hundreds of parameters requiring active management. Chaos Labs CEO Omer Goldberg describes this as moving from static risk management to "dynamic, responsive systems that adapt as markets move."
該公司目前服務於 Aave，協助其管理跨越十多個鏈、總價值鎖定達 190 億美元的資產，每條網路上有數十個市場、需要動態調整的參數多達數百種。Chaos Labs 執行長 Omer Goldberg 將這稱為從靜態風險管理轉型為「能隨市場變化而調整的動態反應系統」。
Beyond lending protocols, Chaos Labs has developed specialized risk frameworks for emerging DeFi primitives including perpetual futures, principal tokens, and liquid staking derivatives. This breadth of application demonstrates how credit risk assessment extends far beyond traditional borrowing and lending.
除了借貸協議以外，Chaos Labs 也為新興的 DeFi 原生產品（如永久期貨、本金代幣，以及流動質押衍生品）開發了專門的風險評估框架。這顯示信用風險評分的應用已遠遠超越傳統借貸。
Credora Network: Consensus-Based On-Chain Ratings
Credora Network：共識型鏈上信用評等
Credora represents a third model: institutional-grade credit assessments deployed directly on-chain through a consensus ratings protocol. Originally founded as X-Margin in 2019 and backed by investors including Coinbase Ventures, S&P Global, and Hashkey, Credora focuses on assessing institutional borrowers for both centralized and decentralized credit markets.
Credora 代表第三種模式：透過共識信用評級協定，將機構級信用評估直接上鏈。Credora 前身為 2019 年成立的 X-Margin，投資者包括 Coinbase Ventures、標普全球（S&P Global）與 Hashkey。Credora 著重於協助中心化及去中心化信貸市場中的機構型借款人進行信用評估。
Credora's methodology combines traditional credit analysis with blockchain-native data. The platform evaluates borrowers across financial strength, debt capacity, governance quality, and market position, producing ratings that map to traditional credit agency scales (AAA to CCC). As of mid-2024, Credora had facilitated over $1.5 billion in loans using its assessment framework.
Credora 的方法融合傳統信用分析與區塊鏈原生數據。該平台從財務實力、負債能力、治理品質、市場地位等多個維度評估借款人，並產出對應於傳統信用評等機構（AAA 至 CCC）的評級。截至 2024 年年中，Credora 已透過其評估機制促成超過 15 億美元的放款。
What distinguishes Credora is its integration with on-chain infrastructure. The company partnered with Space & Time (a decentralized data warehouse) and Chainlink (oracle network) to distribute credit scores directly to smart contracts. When a protocol queries a borrower's credit score, Chainlink Functions pull the data from Space & Time's verifiable database and return it on-chain, enabling real-time credit-based lending decisions.
Credora 的特色是與鏈上基礎設施的深度整合。該公司與 Space & Time（去中心化數據倉儲）、Chainlink（預言機網路）合作，實現將信用分數直接分發給智能合約。當協議查詢借款人的信用分數時，Chainlink Functions 會從 Space & Time 的可驗證資料庫取得數據並回傳鏈上，讓基於信用的放貸決策能夠即時完成。
The metrics Credora provides include:
- Credit Score (0-1000 scale): granular differentiation of borrower creditworthiness
- Rating Agency Equivalent (RAE): mapping to S&P/Moody's scales for institutional comparability
- Implied Probability of Default: statistically-derived default risk over specific time horizons
- Additional Borrow Capacity: scenario analysis showing how much additional debt a borrower could take before materially impacting their score
Credora 提供的評分指標包括：
- 信用分數（0-1000 分）：細緻區分借款人信用狀況
- 信用評等機構對等分（RAE）：對應至 S&P/Moody’s 標準，利於機構比較
- 推算違約概率：依統計推計特定期間內的違約風險
- 額外可借額度：分析不同情境下，借款人在分數大幅受影響前可承擔的額外債務
In February 2025, Credora launched its Consensus Ratings Protocol, a decentralized model that aggregates risk assessments from multiple expert contributors. Rather than relying on a single centralized entity, the protocol enables qualified risk analysts from institutions like Jump Crypto, GSR, and XBTO to provide rating inputs. The system then derives consensus scores through transparent methodology, creating what Credora calls "collective intelligence" for DeFi risk assessment.
2025 年 2 月，Credora 推出其共識型評級協議，以去中心化模式集結多名專家貢獻的風險評估。不再依賴單一中心化單位，而是允許如 Jump Crypto、GSR、XBTO 等機構的合格風控分析師參與評分，系統再運用透明的方法計算共識分數，實現 Credora 所稱的「群體智慧」DeFi 風險分析。
This approach addresses a key criticism of traditional rating agencies: opacity and potential conflicts of interest. By distributing ratings on-chain with transparent methodology and multi-party input, Credora aims to build credibility that can withstand regulatory scrutiny while serving both DeFi-native protocols and institutions exploring on-chain credit.
這種做法回應了傳統評等機構常被詬病的一大問題：不透明及利益衝突。Credora 透過透明的方法及多人貢獻，將評分資料分散上鏈，旨在建立經得起監管部門檢視的公信力，並同時服務 DeFi 原生協議與有意探索鏈上信貸的機構。
In a significant validation of the model's institutional appeal, oracle network RedStone announced in September 2025 that it was acquiring Credora. The merged platform, operating as "Credora by RedStone," combines real-time pricing data with on-chain credit ratings, creating a unified risk management infrastructure for DeFi protocols and institutional allocators.
此一模式的機構吸引力，也於 2025 年 9 月獲得充分驗證——預言機網絡 RedStone 宣布收購 Credora。合併後的平台「Credora by RedStone」將即時報價與鏈上信用評等整合在一起，為 DeFi 協議及機構資本配置者提供統一的風險管理基礎設施。
Comparing Methodological Approaches
方法論比較
These three platforms illustrate the diversity of approaches to on-chain credit rating:
這三個平台展現了鏈上信用評等多元的方法論：
Gauntlet emphasizes protocol-level systemic risk through simulation and backtesting. It's best suited for governance decisions, parameter optimization, and institutional vault management where understanding aggregate risk exposure matters more than individual borrower scoring.
Gauntlet 以模擬與回測為主，強調協議層級的系統性風險。最適合用於治理決策、參數優化與機構金庫管理等情境，當總體風險暴露程度比單一借款人評分更重要時，特別適用。
Chaos Labs focuses on operational automation and real-time risk management. Its oracle-based model serves protocols that need dynamic parameter adjustments to respond to rapidly changing market conditions, effectively turning risk management from a governance process into an automated infrastructure service.
Chaos Labs 聚焦於營運自動化和即時風險管理。其預言機模型適用於需隨市場變動即時調整參數的協議，讓風險管控從治理流程轉型為自動化基礎設施服務。
Credora targets institutional credit assessment with traditional finance comparability. Its consensus protocol and explicit mapping to S&P/Moody's scales make it particularly relevant for bridging DeFi and TradFi, enabling institutions to evaluate on-chain credit using familiar frameworks.
Credora 則以機構級信貸評估為目標，兼具傳統金融的可比性。其共識協議及明確對應 S&P/Moody’s 標準，有助於連結 DeFi 與傳統金融生態，讓機構可用熟悉的架構評估鏈上信用。
All three share common data inputs — on-chain transaction history, collateral composition, liquidation events, protocol interactions — but process this information through different lenses, reflecting distinct use cases within the broader DeFi ecosystem.
三者皆採用相似的數據來源——鏈上交易紀錄、抵押品結構、清算事件、協議互動等——但以不同角度分析，反映其在更廣大的 DeFi 生態中所服務的特殊場景。
Where Models Are Applied: Use Cases & Protocol Impact
應用場景與對協議運作的影響
On-chain credit ratings have moved from theoretical frameworks to practical implementation across multiple DeFi use cases, demonstrating how algorithmic risk assessment can enhance capital efficiency and enable new market structures.
鏈上信用評分已從理論走入實務，廣泛落地於多種 DeFi 應用。這證明演算法風險管理能提升資本效率，並催生嶄新的市場結構。
Scored Lending and Dynamic Collateral
評分型借貸與動態抵押
The most direct application is in lending protocols that adjust terms based on borrower creditworthiness. Clearpool, a decentralized credit marketplace, integrates Credora's on-chain credit scores to enable unsecured and undercollateralized lending to institutional borrowers. When a firm like a trading house or market maker seeks a loan on Clearpool, its Credora rating determines the interest rate, maximum borrowing capacity, and risk premium.
最直接的應用是在會根據借款人信用條件調整借貸條款的協議中。Clearpool 這類去中心化信貸市場即引入 Credora 的鏈上信用分數，為機構型借款人提供無抵押或低質押放款。像交易商或造市商這類機構在 Clearpool 借款時，其 Credora 評級將決定其借款利率、可借額度與風險保費。
This creates a tiered lending market. A borrower with an AA-equivalent rating might access $50 million at 8% APY with 120% collateral, while a BB-rated entity receives $10 million at 12% APY with 150% collateral. The differentiation allows the protocol to optimize risk-adjusted returns for liquidity providers while expanding access to credit for borrowers with strong track records.
這形成階梯式的借貸市場。例如，AA 等級的借款人可用 120% 抵押借 5,000 萬美元，年利率 8%；BB 評級則僅可用 150% 抵押借 1,000 萬美元，年利率 12%。分級作法讓協議能優化流動性供應者的風險調整後報酬，同時擴大信用良好借方的額度可得性。
Several protocols are exploring "hybrid collateral models" where credit scores enable higher LTVs for scored borrowers. Research suggests that wallets with demonstrated low-risk behavior — no liquidation history, consistent debt management, diversified holdings — could safely access 75-80% LTV ratios compared to the standard 60-70% for unscored addresses. This 10-15 percentage point improvement in capital efficiency can make significant differences in profitability for institutional borrowers managing large positions.
多個協議正探索「混合抵押模式」，透過信用評分讓優質借款人適用更高的質押比例（LTV）。研究顯示，那些展現低風險特徵（無清算紀錄、債務管理穩健、資產分散）的錢包可獲得 75-80% LTV，遠高於未評分地址的標準 60-70%。多 10-15 個百分點的資本運用效率，對管理大額部位的機構借款人來說效益顯著。
Institutional-Grade Vaults and Risk-Optimized Strategies
機構級金庫與風險最佳化策略
Gauntlet's institutional vaults demonstrate how credit ratings inform capital allocation at the portfolio level. Rather than simply depositing into highest-yield opportunities, these vaults use Gauntlet's risk scoring to construct optimized portfolios across multiple protocols and chains.
Gauntlet 的機構級金庫 示範了如何利用信用評分引導組合配置。這些金庫不單純存入最高收益標的，而是依據 Gauntlet 風險分數構建涵蓋多條鏈、多種協議的最優化投資組合。
The strategy works as follows: Gauntlet's models continuously assess the credit quality and systemic risk of various lending markets. Funds flow toward protocols with favorable risk-return profiles — perhaps Aave's USDC market on Arbitrum scores 95/100 while Compound's equivalent rates 88/100. The vault overweights the higher-scored opportunity, adjusting dynamically as conditions change.
其策略如下：Gauntlet 持續評分各借貸市場的信用質量與系統風險。例如 Arbitrum 上 Aave 的 USDC 市場獲得 95/100 分，Compound 同類標的為 88/100，資金便加碼投入分數較高者，並隨市況改變動態調整。
This approach has attracted institutional capital from traditional finance entities exploring DeFi yield. Unlike retail users who might chase APY without understanding underlying risks, institutions require sophisticated risk assessment to justify on-chain allocations. Credit ratings provide the analytical framework they need, translating blockchain activity into risk metrics compatible with internal risk management standards.
這讓傳統金融機構在追求 DeFi 收益時更願意配資。與只看 APY 的散戶不同，機構投資人高度重視風險評估，以確保鏈上配置符合理內控標準。信用評分正好提供了將區塊鏈活動轉譯為可比風控指標的分析架構。
Risk Oracles for Automated Protocol Management
風險預言機自動化協議管理
Chaos Labs' deployment with Aave illustrates the operational dimension of credit ratings. Aave's integration of Edge Risk Oracles enables real-time parameter adjustments across the protocol's expansive footprint — 10+ networks, 100+ markets, thousands of variables including supply caps, borrow caps, liquidation thresholds, LTV ratios, and interest rate curves.
Chaos Labs 與 Aave 的合作展現了信用評分的營運面價值。Aave 導入 Chaos Labs 風險預言機 後，能即時自動調整跨 10 多條鏈、100 多個市場、數千個變數（如供給上限、借貸上限、清算門檻、LTV 比例與利率曲線）等參數。
Before risk oracles, parameter changes required:
- Risk team identifies needed adjustment (e.g., reducing liquidation threshold for volatile asset)
- Governance proposal drafted and published
- Community discussion period (typically 3-7 days)
- On-chain vote execution
- Timelock delay before implementation (24-72 hours)
在有預言機之前，協議參數變動需經：
- 風控團隊先識別要調整事項（如降低高波動資產的清算門檻）
- 擬定並發布治理提案
- 社群討論（通常 3-7 天）
- 進行鏈上投票
- 生效前的 Timelock 延遲（24-72 小時）
This 5-10 day cycle meant protocols reacted slowly to market volatility. With automated risk oracles, adjustments happen within predefined boundaries whenever triggers activate, reducing response time from days to
這個 5-10 天的流程讓協議難以即時反應市場波動。有了自動化風險預言機，任何觸發條件進入預設門檻時即可立刻調整，大幅縮短反應時間，從數天下降到 ...minutes.
系統包含了針對極端情況的斷路器設計。如果穩定幣的價格脫鉤超過設定門檻，預言機會自動暫停該市場的新借款，同時仍允許還款和提領。這樣能避免協議在危機事件中累積壞帳——這是在多起 DeFi 事件中從延遲反應導致協議破產所學到的經驗。
Tokenized Credit Markets and Secondary Trading
或許最具變革性的應用，就是可編程條件的通證化信用工具。當信用評分存在於鏈上，協議便能創建根據借款人信用品質自動調整利率、保證金、資產擔保要求的通證化貸款頭寸。
想像一下，一個協議將公司貸款以可交易 NFT 的形式通證化。每個 NFT 代表一筆貸款，所有條款會被寫入 metadata：借款方、利率、到期日、起始時的信用評分。隨著借款人的信用評分因新的鏈上活動或定期重新評估而更新，這個 NFT 的風險特徵也會變化，進而影響其二級市場價格。
這將使傳統需大量摩擦的場外債券，創造出流動性十足的債務工具市場。投資人得以依據風險層級組建貸款組合、對沖風險曝險、或為借款人提供流動性而無需直接參與協議。鏈上信用評分的透明度讓價格發現更加高效——買方確切得知自己承擔何種風險，因為該評分可驗證且可查證。
Impact on Capital Efficiency
這些應用的總體效果，就是提升整體 DeFi 的資本效率。針對有信用評級和無評級的 DeFi 策略的研究顯示，像 Morpho Vaults 這種有評級的協議成長速度可比同類無評級協議快 25%，證明用戶對透明風險評估的需求。
對個別用戶而言，信用評分創造出良好行為的激勵。保持質押資產健康、避免清算、持續良好管理債務，都能直接提升個人評分，取得更優貸款條件。這個行為機制讓 DeFi 從純粹的交易型態轉變為基於聲譽的網絡，儘管這聲譽來自鏈上可驗證的活動，而非主觀的社交線索。
協議方面，基於風險的定價能讓金庫管理更加細緻。協議不必制定過於保守、讓資本無法有效利用的單一條件，而是可以提供差異化條件，在維持安全邊際之下最佳化利用率。隨著 DeFi 的規模成長及流動性競爭加劇，這種方式愈發重要。
為什麼重要：連結 DeFi 與傳統金融
鏈上信用評級的發展，不只是一種對 DeFi 基礎設施的小幅優化——它或許是此產業長遠存活和與傳統金融體系接軌的關鍵。
與傳統信用市場的對比
傳統金融全球配置超過 300 兆美元債務資本，而標準普爾、穆迪、惠譽等機構的信用評級扮演了多元關鍵角色：促成債券市場的價格發現、決定銀行監管資本要求、指引退休基金與保險公司的投資規範，亦為跨法域信用風險評估建立共通語言。
DeFi 從 2019 年幾乎零基礎發展至 2025 年超過 1200 億美元，大多沒有這類基礎設施。過度抵押作為啟動機制雖然奏效，卻嚴重限制可擴展性。每借 1 美元就必須鎖定至少 1.5 美元的資產，降低資本流轉速度，也將沒有大量加密資產的潛在借款人排除在外。
鏈上信用評級有機會打造更高效率市場。如果 DeFi 能建立受機構信賴的標準化風險評估體系，就能觸及傳統金融機構——退休基金、保險公司、主權財富基金等——把關流動資金時所需的嚴謹風險架構。
機構背書：併購與合作
RedStone 併購 Credora（2025 年 9 月）標誌著機構對有評級 DeFi 策略的持續興趣。RedStone 決定將信用評級納入自己的預言機基礎設施，正是建立在風險評估和定價數據同等重要的共識之上。
同樣地，主要金融機構已投入測試依賴可靠風險評分的通證化信用應用。摩根大通 Project Guardian、BlackRock 的 BUIDL 基金、Franklin Templeton 的 OnChain 美國政府貨幣基金，皆是把傳統資產鏈上化的實驗。這些計畫若要規模化發展，勢必仰賴符合機構標準的信用架構。
通證化現實資產（RWA）市場如今已超過 250 億美元，美國國債通證化規模達 66 億，私募信用市場超過 130 億。這些市場要能正常運作，必須有信用評估——投資人購買通證化公司貸款需評判違約風險，放款人用通證化債券作抵押也需準確估價，監管機關則要求公開透明的風險指標。
開啟新的承作模式
鏈上信用分數釋放了現有 DeFi 不存在的商業模式。如 Clearpool 這類平台的成長，體現了規模化機構借貸對無或低抵押信用貸款的需求。做市商、加密原生公司經常需短期流動資金，卻難以鎖住大量抵押品。
對這些借款人的信用貸，能帶給放款人更高的風險調整後報酬（穩定幣 8-12% 年化 vs. 傳統過度抵押僅 4-5%），同時也讓借款人獲得更高效的資金使用。這模型奏效的關鍵，就是信用評分將違約風險具體量化、定價，使風險承擔基於資訊，而非全面保守。
這原則可進一步擴展至散戶。以往 DeFi 幾乎排除了沒有大量加密資產的用戶。鏈上信用分數將來有望讓長期表現穩健的錢包取得小額無抵押貸款，類似於傳統金融的信用卡。雖然法規及法律障礙尚高，技術基礎正逐步完善。
對資金成本的影響
長遠看，對 DeFi 資金成本最深遠的影響或許在此。目前 DeFi 協議為吸引流動性供應者，必須願支付任何有需要的利率，主要依靠使用率曲線與治理投票決定。有了信用評級，協議即可細分市場：低風險借款人拿到較低利率，高風險者則需支付更高利息。
這種分級定價有利於降低低風險用戶的平均借款成本，同時讓願承擔評級風險的流動性供應人獲得優渥報酬。如此的效率提升，能讓 DeFi 在特定場景挑戰傳統借貸，尤其是跨境交易與 24/7 可用性的需求，是傳統金融較弱的領域。
在資金供給面，機構愈來愈將有評級的 DeFi 看作合法的收益選擇。比如，一家 1 億美元保險公司金庫，若能證明其風險接近投資等級公司債，便可能配置 1-2% 於 A 級 DeFi 貸款。這類機構資金流動可大幅加深 DeFi 流動性，並減緩利率波動。
監管融合潛力
全球監管機關正積極探索如何監管 DeFi 和通證化資產。其中一大困難，在於為與加密市場互動的銀行及金融機構設定資本適足率。缺乏標準風險評估時，監管者往往只能全部禁止，或強加過度保守的資本要求，使 DeFi 缺乏吸引力。
鏈上信用評級可為監管方提供所需風險指標，以制定更合適的監理框架。例如某協議被多方獨立分析師評為 A 級，監管機關或許能給予較低的風險權重──這將激勵協議主動引入評級，也激勵評級機構提升自身合規標準。
歐盟 MiCA（加密資產市場法規）及新加坡、香港等地的類似法規皆正著手討論相關議題。隨監管清晰度提升、鏈上信用評級成熟，DeFi 信用市場有機會於傳統金融監管框架下取得合法認可。
風險、限制與注意事項
儘管鏈上信用評級充滿前景，但亦需正視其重大挑戰與限制。這些系統至今仍具實驗性，而普及可能帶來全新風險，甚至未能解決部分根本問題。
Data Quality and CompletenessOn-chain credit ratings face an inherent constraint: they can only analyze data available on public blockchains. While transactions, deposits, borrows, and liquidations are visible, crucial information remains off-chain — company financials, cash flow, real-world assets, legal standing, governance quality, management competence, and external debt obligations.
區塊鏈上的信用評等本質上有一個限制：只能分析公開區塊鏈上的資料。雖然交易、存款、借貸與清算都是可見的，但許多關鍵資訊仍然留在鏈下──例如企業財務狀況、現金流、現實資產、法務狀態、治理品質、經營團隊能力以及對外債務義務等等。
For institutional borrowers, this creates an incomplete picture. A trading firm might have impeccable on-chain history but be facing lawsuits, regulatory investigations, or declining profitability in off-chain operations. Traditional credit analysis incorporates these factors; on-chain models largely cannot. Credora addresses this through supplementary due diligence and privacy-preserving attestations, but the fundamental limitation persists.
對機構型借款方來說，這會導致資訊不完整。某個交易公司鏈上的歷史紀錄或許完美無瑕，但鏈下的運作卻可能正面臨訴訟、監管調查，或是獲利能力下滑。傳統的信用分析會納入這些因素，鏈上模型在很大程度上卻難以涵蓋。Credora 透過額外的盡職調查與保護隱私的認證來改善此問題，但根本的限制依然存在。
For individual wallets, the problem manifests differently. A new wallet with no history receives low scores despite potentially being controlled by a creditworthy individual or entity. Conversely, a wallet with clean history could belong to a sophisticated bad actor who hasn't yet executed their exit scam. The pseudonymous nature of blockchains prevents linking wallet reputation to real-world identity, limiting the credit signal's reliability.
對個人錢包而言，這個問題又以不同方式呈現。全新且沒有歷史記錄的錢包即便其背後持有人或實體具備良好信用，也會獲得低分。相反地，乾淨交易歷史的錢包有可能是尚未執行詐騙計劃的高明壞份子所持有。區塊鏈的偽匿名特性使得錢包信譽無法與真實身份連結，降低信用評等訊號的可靠性。
Model Risk and Transparency
Rating models involve subjective design choices — which variables to weight, how to handle edge cases, what historical periods to analyze, which stress scenarios to simulate. These choices embed assumptions that may not hold during unprecedented market conditions.
評等模型的設計涉及不少主觀選擇——例如要權重哪些變數、如何處理邊緣案例、要分析哪些歷史時期、要模擬哪些壓力場景。這些選擇本身就包含預設假設，但這些假設可能在前所未見的市場狀況下不成立。
Gauntlet's simulations assume certain liquidator behavior patterns, but a black swan event might see coordination failures or deliberate attacks that models didn't anticipate. Chaos Labs' thresholds depend on recent historical volatility, potentially missing low-frequency, high-impact risks. Credora's consensus model assumes expert contributors remain independent and unbiased, but could be manipulated if multiple participants collude.
Gauntlet 的模擬假設清算人的行為模式不變，但在黑天鵝事件下，可能出現協調失靈或是有意攻擊，這些都是模型難以預料的。Chaos Labs 的閾值依賴近期香港交易的歷史波動，可能忽略了低頻高衝擊的風險。Credora 的共識模型則假設專家貢獻者能保持獨立和公正，但若多方串謀也有被操弄的風險。
Model transparency varies significantly across providers. While Credora publishes its methodology frameworks and Gauntlet shares high-level approaches, proprietary models contain trade secrets that limit external validation. Users and protocols must trust that the rating providers have accurately captured risk, creating centralization risk even in nominally decentralized systems.
模型的透明度因服務供應商而大異其趣。Credora 會公開其方法論框架，Gauntlet 也會說明其高層規劃，但專有模型內含商業機密，使外部難以驗證。用戶和協議必須信任評級方能否正確識別風險，即使系統表面上是去中心化，這也產生了新的中心化風險。
Systemic Risk from Correlated Models
A particularly concerning scenario: if many protocols adopt the same credit rating system or similar models, their risk management becomes correlated. When the model indicates reducing exposure to a certain asset or borrower type, multiple protocols might take identical actions simultaneously, creating fire-sale dynamics or liquidity crises.
特別令人擔憂的情境是：若許多協議採用相同或類似的信用評分系統，他們的風險管理就會趨於同步。當模型指示減少對某類資產或借款者的曝險，許多協議可能會同時採取一樣的行動，進而引發連鎖拋售甚至流動性危機。
We've seen analogous failures in traditional finance — Value at Risk (VaR) models used by many banks led to correlated selling during the 2008 financial crisis, exacerbating market crashes. DeFi's interconnectedness through shared collateral and composable protocols could amplify such effects.
傳統金融界已發生過類似的失敗——許多銀行採用的風險價值模型（VaR）引發 2008 年金融危機期間的連鎖賣壓，加劇市場崩潰。DeFi 協議層層交織、抵押物共享，組合性強，這些現象都可能放大上述效果。
Diversification of rating methodologies helps mitigate this risk, but it also creates confusion. If Gauntlet rates a protocol 95/100 while Chaos Labs rates it 78/100, which should users trust? The lack of standardization that provides methodological diversity also undermines the creation of a common risk language.
多元化的評分方法可降低相關性風險，但也會造成困惑。比如 Gauntlet 對某協議評分 95/100，Chaos Labs 卻只給 78/100，使用者該信哪一家？缺乏標準化讓方法能夠多樣化，但同時也阻礙建立共同的風險語言。
Behavioral Risks and Gaming
Credit scores create incentives that participants may game. A borrower anticipating major leverage might carefully maintain perfect behavior to build score, then exploit that reputation in a calculated attack. The challenge is distinguishing between genuine creditworthiness and reputation farming.
信用評分會產生誘因，讓參與者有機可乘。某些借款人若預計往後大舉槓桿，過去就會刻意維持無懈可擊的紀錄來建立高分，等到時機成熟便利用這份信譽進行計算過後的攻擊。難點在於如何區分真實的信用價值與單純的信譽「養分」行為。
On-chain behavior is also easier to manipulate than off-chain credit history. A sophisticated actor could operate multiple wallets, build reputation across all of them through manufactured transaction history, then coordinate defaults. While blockchain transparency makes forensics possible, detection happens reactively after damage occurs.
鏈上行為比起鏈下信用歷史更容易被操弄。高明的行動者可以同時操作多個錢包，以偽造的交易歷史養出良好信譽，再協同毀約。雖然區塊鏈的透明度讓事後司法鑑識成為可能，但這都發生在損失已經造成之後。
Rating systems must also avoid creating perverse incentives for protocols. If a protocol's rating significantly affects its ability to attract liquidity, it might pressure rating agencies to inflate scores or could manipulate observable metrics to game the model. This dynamic closely mirrors the conflicts of interest that plagued traditional credit agencies during the 2008 crisis.
評級系統也必須避免對協議產生反向激勵。如果協議的評級大幅影響其吸引流動性的能力，協議便有動機施壓於評級機構提高分數，或操弄外部可見的數據來作弊模型。這種情形與 2008 年金融危機時傳統信評機構的利益衝突如出一轍。
Regulatory and Legal Questions
The regulatory status of on-chain credit ratings remains uncertain across jurisdictions. Questions include:
區塊鏈信用評級的監管地位在各國仍不確定。相關問題包括：
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Are these ratings considered "investment advice" or "credit rating activities" requiring registration and oversight? In many jurisdictions, credit rating agencies face stringent regulations following their failures during the 2008 crisis. On-chain rating providers may eventually face similar requirements.
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這些評級是否屬於「投資建議」或「信用評級業務」，需註冊和受監管？ 在許多司法管轄區，信評機構因 2008 年危機後遭受嚴格監管。區塊鏈評級方未來可能要遵循類似規定。
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Do lending protocols using ratings assume liability for inaccurate assessments? If a protocol adjusts a borrower's terms based on a credit score that proves wrong, who bears responsibility for resulting losses?
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借貸協議據評分調整條件時，若評分失準，該由誰負責？ 若協議依據信用評分調整借款條件，卻造成損失，責任歸屬如何認定？
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Are borrowers protected under consumer lending regulations? If credit scores affect access to financial services, they might trigger anti-discrimination laws, fair lending requirements, or right-to-explanation rules in certain jurisdictions.
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信用分數影響借款人取得金融服務時，是否須適用消費者保護法令？ 若信評左右金融機會，或需符合相關禁歧視、公平放貸、解釋權等法規。
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How are cross-border credit assessments handled? A rating provider based in Singapore assessing a U.S. protocol lending to European borrowers operates in a regulatory gray area with unclear jurisdictional authority.
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如何處理跨境信用評估？ 比如總部設於新加坡的信評方，替美國協議評級，該協議又服務歐洲借款者，這樣的情境法律責任模糊不清。
The Reliability Gap
Perhaps the most fundamental limitation: on-chain credit ratings lack the decades of data and stress-testing that traditional systems have undergone. S&P's investment-grade corporate default rate is historically under 0.2% annually because the agency has refined its models across multiple business cycles. On-chain ratings have existed for at most a few years, through limited market conditions.
或許最根本的缺陷在於：鏈上信用評分缺乏傳統機構經歷多個商業循環、數十年來淬煉出的龐大資料和壓力測試。S&P 投資級公司違約率歷史年均不到 0.2% ，因其模型經多年反覆優化。鏈上評級多半只發展數年，僅經歷有限市場情境。
DeFi hasn't yet experienced a true systemic crisis equivalent to 2008 — a scenario where credit markets freeze, liquidations cascade across protocols, and flight-to-safety causes mass deleveraging. Until rating models are tested in such conditions, their reliability remains speculative. The March 2020 crash and subsequent events provided some stress tests, but they may not represent tail-risk scenarios that determine whether ratings truly capture risk.
DeFi 產業至今未經歷過如同 2008 金融危機那種真正的系統性動盪──即信用市場凍結、連環清算、資金避險潮引爆廣泛去槓桿等場景。在這種環境下測試前，評級模型的可靠性終究只是假說。2020 年三月閃崩及其後續事件固然是壓力測試，但未必能反映極端尾部風險，難以判斷信用評分是否真的能抓出風險。
Research like "SoK: Decentralized Finance (DeFi)" by Werner et al. systematizes these challenges, distinguishing between technical security (smart contract exploits, oracle manipulation) and economic security (market manipulation, flash loans, governance attacks). Credit ratings primarily address economic security but remain vulnerable to technical failures that could render risk assessments meaningless if underlying protocols are compromised.
如 Werner 等學者的 "SoK: Decentralized Finance (DeFi)" 等研究將這些挑戰系統化，區分技術安全（如智能合約漏洞、預言機操控）和經濟安全（如市場操縱、快閃貸、治理攻擊）。信用評級主要針對經濟安全，但若協議本身遭技術性破綻攻擊，原有的風險評估即失去意義。
What Users and Protocols Should Know
As on-chain credit ratings gain adoption, participants need frameworks for evaluating and utilizing these systems effectively.
隨著鏈上信評被更多採用，參與者需要系統化框架來有效評估與運用此類系統。
For Users: Understanding Your Score
When encountering a DeFi protocol that displays credit scores or adjusts terms based on ratings, users should investigate several key factors:
What drives the score? Understand which on-chain activities matter. Most models weigh borrowing history, liquidation events, asset diversity, and transaction patterns, but the specific formulas vary. Some systems penalize any liquidation heavily, while others distinguish between forced liquidations due to volatility versus irresponsible over-leverage.
遇到顯示信評分數或根據評分調整條件的 DeFi 協議，請留意下列重點：
分數由什麼驅動？ 了解哪些鏈上行為最重要。多數模型會考慮借貸紀錄、清算事件、資產多元性和交易模式，但每家的細節不同。有的系統只要清算就重罰，有的則會區分清算成因（短線波動與過度槓桿無差別處理）。
How often does the score update? Real-time scoring systems respond immediately to on-chain activity, while periodic assessments might lag by days or weeks. This affects strategies — you can't build reputation overnight in most systems, but you can also avoid sudden score drops from temporary positions.
分數多久更新一次？ 即時評分會同步鏈上行為，週期性評估則可能落後數日甚至數週。這會影響你的策略——多數系統信用無法一夕建立，但也可以防止短暫操作造成分數暴跌。
Can you access your own score? Transparency varies. Some platforms like Cred Protocol provide user dashboards showing credit scores and the factors affecting them. Others operate opaquely, with scores visible only to protocols querying the data. Users benefit from systems that explain their risk profile and suggest improvement paths.
你看得到自己的分數嗎？ 透明度差異很大。有些平台如 Cred Protocol 提供用戶儀表板，詳列信用分數及其影響因子；有的則未公開，用戶只知道協議端是否讀取分數。有說明風險概況和改善建議的系統，對用戶最有幫助。
What's the score's track record? Newer systems lack historical validation. Ask: has this rating model predicted defaults accurately? How did scores correlate with actual outcomes during previous market stress? Providers with transparent backtesting and post-implementation analysis offer more credibility.
評分歷史表現如何？ 新興系統大多缺乏長期背測經驗。請詢問：這種模型預警違約的準確度如何？在歷史市場壓力時期，分數跟實際違約狀況符合嗎？有公開回測與事後分析的方案，可信度自然較高。
Are there appeals or corrections? If your score seems inaccurate — perhaps due to a one-time event or data error — can you contest it? Consumer credit systems offer dispute mechanisms; on-chain equivalents should consider similar processes.
有申訴與修正管道嗎？ 若分數因偶發事件或數據誤差而失真，能否申訴或更正？傳統消費者信評有爭議機制，鏈上信評可引入類似機制。
For Protocols: Evaluating Rating Services
DeFi protocols considering credit rating integration should assess several dimensions before deployment:
考慮導入信用評分的 DeFi 協議，應從下列面向審查評級服務：
Methodology rigor: Request detailed documentation of the rating model. How are default probabilities calculated? What historical data informs the model? What stress scenarios are tested? A robust provider should offer comprehensive methodology papers, not just marketing materials.
方法學嚴謹度： 要求詳細的模型文件。違約機率怎麼算？模型參考哪些歷史數據？壓力場景涵蓋哪些？有扎實方法論白皮書而非只有行銷材料，才算負責。
Data sources: Understand what information feeds the ratings. Pure on-chain data provides transparency but limited scope. Hybrid approaches incorporating off-chain verification offer richer context but introduce trust assumptions. Evaluate whether the data aligns with your risk concerns.
資料來源： 了解信評依據哪些數據。純鏈上資料透明但範圍有限，混合鏈下驗證雖資訊豐富但需多一層信任假設。務必評估其數據能否滿足你真正的風險管理需求。
Transparency vs. proprietary balance: Complete transparency allows community validation but may enable gaming. Fully proprietary models prevent verification. The optimal balance depends on use case, but critical components should be publicly documented even if full implementation details remain confidential.
透明化與專有權平衡： 完全透明易於社群驗證，但也提高被惡意利用的風險；全專有模式則無法被外界審核。最理想的平衡取決於應用場合，至少重要核心邏輯應公開，即便細節保密。
Governance and independence: Whocontrols the rating provider? How are model updates decided? Can the provider be pressured by rated entities? Independent governance structures with diverse stakeholder input build credibility, while centralized control raises conflict-of-interest concerns.
誰在控制評等提供者？模型如何決定更新？評等對象是否能對提供者施壓？具有多元利害關係人參與的獨立治理架構能建立可信度，而集權式控制則會引發利益衝突疑慮。
Integration costs: Beyond direct fees, consider technical complexity. Does integration require custom smart contract modifications? How much gas do score queries consume? What happens if the rating service experiences downtime or price feed failures?
**整合成本：**除了直接費用外，也要考慮技術複雜性。整合過程是否需要自訂智能合約？查詢分數會消耗多少 gas 費？若評等服務發生停機或價格來源失效，會有什麼影響？
Regulatory compliance: Evaluate the provider's legal structure and compliance posture. As regulations evolve, partnerships with well-structured entities reduce protocol risk. Some regions may eventually prohibit using unregistered rating services.
**合規性：**應評估提供者的法律結構與合規狀況。隨法規演變，與架構健全的單位合作可降低協議風險。有些地區日後可能會禁止使用未登記的評等服務。
Scalability and coverage: Does the provider rate the assets and chains relevant to your protocol? Can the system scale as your protocol grows? Comprehensive coverage reduces the need for multiple rating partners and simplifies parameter management.
**可擴展性與覆蓋範圍：**提供者是否有評價你協議涉及的資產與鏈？系統能否隨協議成長而擴展？完整的覆蓋能減少多頭合作與簡化參數管理。
For Investors: Rating's Role in Due Diligence
Institutional and sophisticated retail investors can leverage ratings as one input among many:
給投資人的建議：評等於盡職調查的角色
機構與高階散戶投資人可將評等作為眾多參考指標之一：
Risk-adjusted yield analysis: A protocol offering 10% APY with an AA rating provides very different risk exposure than one offering 10% with a BB rating. Compare yields across rating tiers to identify opportunities where risk-return ratios seem misaligned.
**風險調整後的收益分析：**一個提供 10% 年化收益且被評為 AA 等級的協議，與同樣收益僅獲 BB 評級的專案，風險程度截然不同。可跨評等等級比較收益，找出風險報酬不均的投資機會。
Portfolio construction: Build diversified exposure across rating grades and methodologies. Rather than concentrating in highest-rated opportunities, consider balanced allocations that capture higher yields from lower-rated assets while maintaining safety buffers.
**投資組合建構：**分散配置於不同評等及方法學下的資產。不必只集中在最高評級，而是嘗試在風險可控下，適度配置部分低評級但高收益資產，提升整體回報。
Model diversity: Don't rely on a single rating provider's assessment. If Gauntlet, Chaos Labs, and Credora all rate a protocol similarly, that provides more confidence than relying on one source. Significant divergence between providers warrants investigation.
**模型多樣性：**不要只依賴單一評級機構。若 Gauntlet、Chaos Labs 及 Credora 對某協議評級一致，可信度會高於僅參考其中一家；若評級差異大，則有必要深入了解原因。
Independent verification: Ratings complement, but don't replace, personal due diligence. Review protocol audits, governance structures, team backgrounds, and community health independently. High ratings don't eliminate smart contract risk, regulatory risk, or execution risk.
**獨立驗證：**評級僅作為輔助，並非取代自己的盡職調查。請獨立檢視協議審計、治理架構、團隊資歷與社群狀況。高分評級也無法排除智能合約風險、合規風險與執行風險。
Historical correlation: Track how ratings correlate with actual outcomes over time. Which providers' ratings best predicted defaults or protocol issues? Adjust confidence in different systems based on empirical track records.
**歷史相關性：**追蹤評級與實際狀況的長期關聯。哪些評級機構最能預測違約或協議問題？可依據歷史數據調整對各種系統的信任程度。
Future Outlook
On-chain credit ratings are likely entering a period of rapid evolution and adoption as DeFi matures and converges with traditional finance. Several trends will shape this trajectory.
未來展望
鏈上信用評等隨著 DeFi 逐漸成熟並與傳統金融融合，正進入快速發展及應用時期。以下幾大趨勢將影響其未來發展。
Fully Decentralized Credit Scores
Current systems largely depend on centralized entities — companies like Gauntlet, Chaos Labs, and Credora that process data and produce ratings. The next generation may be fully decentralized, with credit scoring protocols operated by token-governed DAOs and consensus mechanisms.
完全去中心化的信用分數
目前的系統多仰賴中心化公司，如 Gauntlet、Chaos Labs 和 Credora，負責資料處理與評級。下一代方案有望徹底去中心化，評分協議由代幣治理 DAO 及共識機制運作。
Early examples like Credora's Consensus Ratings Protocol point toward this model. Multiple independent contributors provide rating inputs, and algorithmic aggregation produces final scores. This approach could leverage mechanisms like staked validation (rating providers stake tokens that can be slashed for poor predictions) or futarchy (prediction markets determine credit quality).
早期案例如 Credora 推出的 Consensus Ratings Protocol，即朝此方向發展。多方獨立的參與者供應評級資料，最終由演算法聚合產生分數。此種模式可導入質押驗證（評級者須質押代幣，錯誤預測會被沒收）或未來學（由預測市場決定信用好壞）等機制。
Research on wallet reputation systems like zScore demonstrates how machine learning models can analyze behavioral patterns across DeFi protocols, assigning reputation scores based on liquidity provision, trading discipline, and protocol engagement. These models could run entirely on-chain or through decentralized oracle networks, eliminating reliance on centralized rating agencies.
像 zScore 這類[關於錢包聲譽系統的研究]，展現了機器學習模型可以分析 DeFi 參與者的行為模式，根據其供給流動性、交易紀律與參與度來給予聲譽分數。這類模型可完全在鏈上運行，或透過去中心化預言機網路，消除對傳統集中式評級機構的依賴。
The challenge is maintaining accuracy and accountability without centralized oversight. Traditional credit agencies' reputations provide incentive alignment; decentralized alternatives need different mechanisms to ensure contributors perform rigorous analysis rather than superficial consensus-seeking.
其挑戰在於，缺乏中心化監督，如何維持精準性與問責性。傳統評級機構的名譽會帶來激勵對齊；去中心化方案則需建立其他機制，確保貢獻者進行嚴謹分析，不淪為表面共識。
User-Level Credit Portability
Currently, most credit systems operate at the protocol or institutional borrower level. The next phase may extend to individual wallet reputation that follows users across DeFi.
用戶級信用可攜性
目前多數信用評等做在協議或機構借方層級。下一波發展可能擴展至個人錢包的聲譽系統，讓信用記錄伴隨用戶流動於各 DeFi 平台。
Imagine a universal credit score that travels with your wallet — a composite reputation earned through responsible DeFi participation that any protocol can query. This score might factor in your borrowing history on Aave, liquidity provision on Uniswap, governance participation in multiple DAOs, and transaction patterns across chains. Protocols could offer individualized terms based on your portable score rather than applying blanket parameters.
設想一個通用的信用分數，隨你的錢包移動—這個複合聲譽是你在 DeFi 中負責任參與所累積，任何協議都可查詢。此分數會考慮你在 Aave 的借貸紀錄、Uniswap 的流動性供給、參與多個 DAO 治理及跨鏈交易模式。協議可依你的此分數提供專屬條件，而非一律適用同樣標準。
Such systems raise identity and privacy considerations. Users might want separate wallets for different purposes, compartmentalizing their DeFi activity. Privacy-preserving technologies like zero-knowledge proofs could enable selective disclosure — proving you have a credit score above a threshold without revealing the exact score or underlying activity. Projects exploring zero-knowledge credit verification are working to bridge traditional FICO scores to on-chain reputation using cryptographic proofs.
這類系統涉及身分與隱私議題。用戶可能會為不同目的分開使用多個錢包，區隔 DeFi 活動。隱私保護技術（如零知識證明）可實現選擇性揭露——證明你的信用分數達標，而不必揭露分數細節或底層活動。一些專案正探索運用零知識驗證，將傳統 FICO 信評以加密證明方式橋接到鏈上信譽。
Tokenization of Rated Credit Assets
The convergence of on-chain credit ratings and real-world asset tokenization will likely produce new financial instruments. We're already seeing tokenized U.S. Treasuries reaching $6.6 billion and private credit tokenization exceeding $13 billion, but these markets still lack robust secondary trading infrastructure.
信用資產評級與代幣化
鏈上信用評等與實體資產代幣化的融合，將催生全新金融工具。目前[美國國債代幣化規模已達 66 億美元]，[私人信貸代幣化超過 130 億美元]，但相關市場的二級流通基礎仍不足。
Credit ratings will enable deeper secondary markets for tokenized debt. An investor buying a tokenized corporate loan benefits from knowing its credit quality, just as bond investors rely on ratings for traditional corporate debt. This creates price discovery mechanisms and liquidity for assets that historically traded over-the-counter.
信用評等可推動代幣化債務的二級市場發展。投資人購買代幣化企業貸款時，能像買傳統公債一樣，參考信用評級。這為原本場外交易的資產類型創造市場定價機制與流動性。
We may see DeFi protocols that specialize in packaging rated credit assets into tranches — senior tranches with A-grade ratings offering lower yields, junior tranches with lower ratings offering higher yields but greater risk. This structured credit approach, common in traditional asset-backed securities, becomes programmable through smart contracts and transparent through on-chain ratings.
未來或將出現專注於結構化信用資產的 DeFi 協議，將評級過的資產打包分層——高級分層給予 A 級評等，收益較低；次級分層評等較低、收益更高但風險大。這種結構化信用方式在傳統資產證券化很常見，結合智能合約與鏈上評級後，更具透明與可編程性。
The total addressable market is enormous. Global credit markets exceed $300 trillion; even capturing 1% of this activity on-chain would dwarf current DeFi scale. Credit ratings are essential infrastructure for that migration to occur.
這是個巨大的潛在市場。全球信貸市場規模超過 300 兆美元，哪怕僅有 1% 上鏈，也將遠超當前 DeFi 規模。信用評級將成為這一遷移過程的關鍵基礎建設。
Regulatory Integration
As jurisdictions develop frameworks for digital assets, on-chain credit ratings will likely face formalized regulation. The outcome could take several forms:
法規整合
隨國家陸續訂立數位資產監理規範，鏈上信用評級預料將接受正式的法律監管。未來可能出現以下幾種情況：
Licensing requirements: Credit rating providers might need official registration and oversight, similar to Nationally Recognized Statistical Rating Organizations (NRSROs) in the United States. This would impose compliance costs but also provide regulatory clarity and potentially unlock institutional adoption.
**執照要求：**評級機構可能需正式註冊和接受监督，類似美國 NRSROs 制度。雖會增加合規成本，但可帶來法規明確性，吸引機構採用。
Self-regulatory organizations: The industry might form standards bodies that establish best practices, methodology requirements, and ethics codes. This approach could satisfy regulators' oversight needs while maintaining flexibility and innovation.
**自律組織：**產業可能成立標準機構，建立最佳實務、方法論要求及倫理準則。此法能回應監管需求，又保留彈性與創新空間。
Safe harbor provisions: Regulators might create exemptions for on-chain ratings that meet certain transparency and governance criteria, recognizing that decentralized systems differ from traditional agencies and merit different treatment.
**安全港條款：**監管機關可能對於特定透明與治理標準的鏈上評級另設豁免，體認去中心化系統與傳統機構不同，須差異化規範。
Integration with banking regulations: If on-chain credit ratings achieve regulatory recognition, they could factor into capital adequacy calculations for banks holding tokenized assets or participating in DeFi. This would accelerate institutional adoption by making rated DeFi positions capital-efficient.
**與銀行監理整合：**若鏈上評級獲官方認可，有機會納入銀行業持有代幣化資產或參與 DeFi 的資本適足率計算，進一步推動機構採用。
The Markets in Crypto-Assets (MiCA) regulation in the EU and proposed frameworks in Singapore, Hong Kong, and Japan suggest that major financial centers are developing coherent approaches to crypto regulation. On-chain credit ratings that meet emerging standards could achieve global recognition, facilitating cross-border credit flows.
歐盟 MiCA 法規，以及新加坡、香港、日本等地提出的框架，都顯示主要金融中心正制訂具一致性的加密貨幣監管方法。符合新興標準的鏈上信用評級，有機會獲得全球認可，促進跨境信貸流動。
DeFi in 3-5 Years
Looking forward, a mature DeFi ecosystem with widespread credit rating adoption might feature:
3~5 年後的 DeFi
展望未來，當 DeFi 生態體系成熟並廣泛採用信用評級時，可能會出現如下特徵：
Tiered lending markets where borrowers are segmented by credit quality, with interest rates, LTVs, and terms varying accordingly. Over-collateralized lending persists for unrated or low-rated borrowers, while creditworthy participants access efficient capital.
**分級借貸市場：**借貸方按信用分級，利率、貸款成數(LTV)及條件各異。未評等或低評等仍需高額超額質押，優質信用用戶則可有效率地取得資金。
Institutional participation at scale as pension funds, insurance companies, and asset managers allocate portions of portfolios to rated DeFi opportunities that fit within existing risk management frameworks. This brings trillions in traditional capital to on-chain markets.
**大規模機構參與：**退休基金、保險公司、資產管理機構等可將部分資金，配置到已通過評級、且符合法人風控標準的 DeFi 產品，將傳統金融大量資本引入鏈上市場。
Seamless TradFi-DeFi integration where tokenized traditional assets (bonds, loans, stocks) trade alongside crypto-native assets in unified markets. Credit ratings provide the common risk language enabling comparison and portfolio optimization across both worlds.
**傳統與 DeFi 無縫銜接：**傳統資產(如債券、貸款、股票)完成代幣化後，與原生加密資產在同一市場交易。信用評級成為雙方共通的風險語言，有助於跨市場比較與資產配置最佳化。
Programmable credit products where smart contracts automatically adjust lending terms, collateral requirements, and risk parameters based on real-time credit score updates. This automation reduces operational overhead and enables sophisticated strategies impossible in traditional finance.
**可編程信用產品：**智能合約能依即時信用分數，自動調整借貸條件、質押要求與風險參數，降低營運成本並可實現傳統金融難以達成的複雜策略。
Reduced collateral requirements as credit scoring becomes more accurate and accepted, allowing progression from 150% overcollateralization toward models where highly-rated borrowers access 90% or even uncollateralized loans.
**質押要求減少：**隨著信用評分日益精確與普及，質押成數可從現行 150% 超額質押，進步到高評級用戶僅需 90% 甚至零質押貸款。
Democratic access to credit where individuals and small businesses globally can build
**普惠信貸：**讓全球個人與中小企業都能建立...on-chain credit histories and access financing without traditional banking relationships, reducing financial exclusion.
Final thoughts
區塊鏈上的信用評等代表著 DeFi 從實驗性金融原型進化為全球可擴展信貸市場的關鍵基礎設施層。藉由為去中心化借貸帶來透明、數據驅動的風險評估，這些系統解決了一個長期限制 DeFi 成長潛力的基本低效率問題。
這個領域仍在萌芽階段，不同的方法學彼此競爭，過往紀錄尚未獲得充分驗證，且在數據品質、模型透明度及系統性風險等方面都存在顯著限制。然而，發展趨勢已相當明確：主要協議正在整合信用評等，機構資本愈發重視健全的風險框架，而實體資產數位化也正在創造原生於鏈上的吸引人信用評等應用場景。
若要讓 DeFi 成熟到超越過度抵押借貸、實現高效率且可普及的全球信貸市場，標準化的風險評分是不可或缺的。就如同價格預言機成為推動 DeFi 第一波成長的關鍵基礎設施，信用評等很可能也會成為第二波成長的根基—推動低抵押借貸、代幣化債券市場以及機構級大規模採用。
用戶和協議在面對鏈上信用評等時，應保持適切的審慎態度。理解不同模型的優勢與局限，分散採用不同評級機構，並持續自主風險評估。如同所有新興科技，早期採用既有風險，但忽視這些基礎設施，也意味著將被競爭者所利用而失去優勢。
未來幾年，將決定鏈上信用評等能否達到穩健與廣泛接受的程度，進而成功橋接 DeFi 與傳統金融。目前技術基礎正逐步建立，監管架構正逐步明朗，機構需求已經存在。剩下的課題，是執行力——評等機構能否交付準確、值得信賴的風險評估，經得起壓力測試，並獲得加密原生用戶與傳統金融機構的信心？
如果成功，鏈上信用評等將被視為讓 DeFi 從小眾加密現象轉型為傳統信貸市場正規替代方案的關鍵基礎設施，進而在全球拓展金融可及性與效率。反之，若因預測不準、監管打壓或系統性失誤而失敗，DeFi 可能會繼續受限於過度抵押借貸與邊緣應用。這是一場高賭注、大挑戰、但同時蘊含巨大機會的賽局。