The Future of Credit Scoring: On-Chain Transaction Graphs

FICO scores are a lagging indicator, based on a narrow data set and prone to systemic bias. They are inaccessible to 1.7B+ unbanked adults and useless for assessing DeFi protocols or DAO contributors.

• Ignores 99% of on-chain financial behavior (LP positions, governance, repayment history).
• Creates a ~12-24 month latency for new entrants to establish credit.
• Centralized control by three agencies creates a single point of failure and censorship.

A user's wallet history is a public, verifiable financial graph. By analyzing patterns across Ethereum, Solana, and Layer 2s, we can generate dynamic, context-specific scores.

• Real-time solvency proofs via positions in Aave, Compound, and MakerDAO.
• Reputation graphs from consistent participation in Uniswap governance or Optimism Quests.
• Portable identity that protocols like Goldfinch or Maple Finance can permissionlessly query.

This is the trillion-dollar use case. Transaction graphs enable risk-based pricing for loans, moving beyond today's inefficient over-collateralization (~150%+).

• Reduce capital inefficiency by ~50% for borrowers with proven on-chain history.
• Unlock ~$100B+ in latent borrowing capacity currently locked as excess collateral.
• **Protocols like EigenLayer can use these scores for slashing insurance, and Circle for CCTP-based credit lines.

Public graphs are also attack surfaces. The winning model will balance transparency with user control, using zero-knowledge proofs and decentralized identity.

• ZK-proofs (e.g., zkSNARKs) allow proving creditworthiness without exposing full history.
• Sybil resistance requires integrating proof-of-personhood systems like Worldcoin or BrightID.
• Without this, the system is vulnerable to wash trading and reputation farming.

Scoring logic must be a decentralized, updatable primitive. Think Chainlink Functions for computation, The Graph for indexing, and EigenLayer AVSs for cryptoeconomic security.

• Modular scoring stacks let each protocol (e.g., Aave GHO) customize risk parameters.
• Oracle networks provide cryptoeconomic guarantees against data manipulation.
• Creates a market for competing scoring algorithms, verified on-chain.

On-chain credit isn't just a DeFi feature; it's a new primitive for global finance. It flattens access and creates a single, programmable layer for risk.

• Enables cross-chain underwriting for real-world assets (RWAs) via protocols like Centrifuge.
• Turns any wallet into a capital-efficient balance sheet.
• Final step in replacing the legacy financial stack with a transparent, algorithmic alternative.

Traditional credit scores are a single, opaque number that fails to capture on-chain behavior. Lenders see a wallet's balance, not its transaction graph, social connections, or DeFi history, leading to massive under-collateralization or outright exclusion.

• Misses Context: A wallet with high NFT volatility looks identical to one with steady DCA habits.
• No Composability: Scores are siloed, preventing protocols like Aave or Compound from building custom risk models.

Restaking capital to cryptographically attest to a user's creditworthiness. Protocols like EigenLayer and EigenDA enable Actively Validated Services (AVSs) where operators stake to run credit oracle networks.

• Sybil Resistance: High stake requirement for attestors prevents spam and low-quality scores.
• Modular Data: AVSs can specialize in subgraphs (e.g., GMX perpetuals history, Uniswap LP behavior) for hyper-granular risk assessment.

Credit becomes a composable data layer. Developers query specialized subgraphs (via The Graph) to build scores, similar to how Uniswap uses oracles for price feeds.

• Composable Risk: A lending protocol like Compound can mix a Goldfinch repayment subgraph with a Safe{Wallet} social recovery graph.
• Real-Time Updates: Graph indexes update on new blocks, enabling dynamic credit lines that adjust with user behavior, moving beyond static MakerDAO CDP models.

Protocols like Arcade and Spectral are building on this graph data to issue undercollateralized loans and programmable credit scores (Nova). This unlocks capital efficiency for the entire DeFi stack.

• Cross-Protocol Portability: A credit score earned via Aave repayments can be used to rent an NFT in reNFT.
• Programmable Terms: Scores trigger automatic actions: a downgrade could reduce a Maple Finance loan-to-value ratio in real-time.

Graphs enable privacy-preserving proof-of-history. Users can generate ZK proofs (via zkSNARKs or RISC Zero) of specific graph properties (e.g., "I repaid 10 loans") without revealing their entire transaction history to Chainlink oracles.

• Selective Disclosure: Prove creditworthiness to a Circle-backed lender without exposing every USDC transaction.
• Soulbound Graphs: Non-transferable credentials (SBTs) issued by protocols like Galxe become verifiable nodes in a private credit graph.

AI agents with on-chain treasuries will need automated credit. Transaction graphs allow these agents (imagine an AutoGPT with a wallet) to establish a verifiable financial reputation, enabling them to borrow from MakerDAO or provide liquidity on Uniswap V4.

• Machine-Readable Reputation: An agent's graph becomes its CV, auditable by any counterparty.
• New Economic Actors: Creates a market for non-human entities to participate in DeFi, moving beyond the limitations of today's Compound or Aave human-centric models.

The core premise of on-chain identity is easily gamed. A user can spin up thousands of wallets with fabricated transaction histories for less than the cost of a coffee. Without a robust, Sybil-resistant identity layer like Worldcoin or Idena, transaction graphs measure capital and patience, not creditworthiness.

• Sybil Cost: <$10 to create 100+ wallets.
• Real-World Impact: Protocols like Aave Arc and Maple Finance cannot rely on raw on-chain data alone.

DeFi and NFT markets are polluted with wash trading for rewards farming and market manipulation. Over 50% of DEX volume on some chains is inorganic. A credit model trained on this data will mistake a mercenary farmer for a high-value user, leading to catastrophic underwriting errors.

• Data Corruption: >50% of volume can be fake on emerging L2s.
• Protocol Risk: Lending pools like Compound or Euler would misprice risk.

The most sophisticated users and institutions will use zk-proofs, Tornado Cash, and private L2s like Aztec. This creates a perverse incentive: honest actors obfuscate their history, while Sybils flaunt transparent, fake graphs. The resulting dataset is adversarially selected and worthless for underwriting.

• Data Gap: High-quality users opt-out via zk-SNARKs.
• Adversarial Selection: Only low-quality data is fully visible.

If an on-chain credit score gains real economic value (e.g., for loan rates), it becomes a direct financial attack vector. Actors will manipulate oracles (like Chainlink) feeding data to the model, or execute complex DeFi transactions designed purely to game the scoring algorithm, similar to exploits against MakerDAO's collateral system.

• Attack Surface: Score = Oracle-Dependent.
• Precedent: $100M+ in oracle manipulation losses historically.

On-chain scoring operates in a global regulatory gray zone. Using it for "off-chain" credit decisions (e.g., mortgage applications) may trigger FCRA, GDPR, or ECOA violations. Protocols like Goldfinch or Centrifuge that bridge to real-world assets face severe legal risk if their scoring model is deemed discriminatory or non-compliant.

• Legal Risk: Violates FCRA/GDPR.
• Market Limit: Confined to pure DeFi use cases.

Creditworthiness is not transitive across contexts. A genius Uniswap LP may be a reckless NFT floor trader. A single, monolithic score fails under DeFi's hyper-composability. The system needs context-specific models (borrowing vs. insurance vs. employment), which fragments liquidity and utility, defeating the purpose of a universal graph.

• Context Failure: 1 score ≠ all risks.
• Fragmentation: Dozens of context-specific models needed.