Reputation-Backed Loan

This foundational metric analyzes the frequency, consistency, and total value of a wallet's on-chain activity over time. It assesses financial behavior patterns.

• Key Indicators: Total ETH volume transacted, number of transactions per month, and active days on-chain.
• Purpose: Establishes a baseline of financial activity and reliability. A long, consistent history of significant volume is a strong positive signal.

Measures the depth and quality of a user's engagement with specific DeFi protocols. It goes beyond simple usage to evaluate sophisticated behavior.

• Key Indicators: Liquidity provision history, governance participation, repeated borrowing/repayment cycles on lending platforms, and use of advanced features.
• Purpose: Identifies experienced, vested users who are less likely to default due to their established stake and understanding of a protocol's ecosystem.

Evaluates the verifiable identity and network connections of a wallet, moving beyond pseudonymous activity to establish real-world accountability.

• Key Indicators: Soulbound Tokens (SBTs) for credentials, DeFi Soul or Gitcoin Passport scores, verified social media attestations, and connections to other reputable wallets or DAOs.
• Purpose: Reduces anonymity risk by tying reputation to a persistent, verifiable identity, making default more costly to one's digital reputation.

The most direct metric for lending, this tracks a user's historical performance with debt obligations on-chain.

• Key Indicators: Timely repayment of previous loans (from protocols like Aave or Compound), credit delegation history, and a record of zero liquidations.
• Purpose: Directly predicts future repayment behavior. A clean repayment history is the strongest single indicator for underwriting a reputation-backed loan.

Assesses the risk profile and financial stability of a user's on-chain portfolio, not just its total value.

• Key Indicators: Portfolio concentration (avoiding over-exposure to a single volatile asset), ratio of blue-chip to speculative assets, and overall portfolio volatility over time.
• Purpose: Evaluates the borrower's ability to withstand market downturns without being forced into default, indicating prudent financial management.

A meta-metric designed to detect and penalize attempts to artificially inflate reputation by creating multiple fake identities (Sybils).

• Key Indicators: Analysis of funding sources (e.g., direct from CEX vs. airdrop farming), cluster analysis of wallet connections, and behavior patterns typical of bots or farming scripts.
• Purpose: Ensures the reputation system's integrity by filtering out low-quality, manipulative actors, protecting the lending pool's capital.

A core vulnerability is the Sybil attack, where a user creates multiple fake identities to build false reputation and borrow more than their true creditworthiness allows. Mitigation relies on robust identity proofing mechanisms, such as:

• Soulbound Tokens (SBTs) for non-transferable attestations.
• Integration with verifiable credentials from established Web2 platforms (e.g., GitHub, Twitter).
• Zero-knowledge proofs (ZKPs) to prove traits (e.g., account age, transaction volume) without revealing personal data.

The loan's terms depend on external reputation data oracles. Key risks include:

• Data manipulation: The oracle's source data (e.g., social media scores, on-chain history) being gamed or corrupted.
• Centralization: Reliance on a single oracle creates a critical point of failure.
• Latency & Freshness: Stale reputation data may not reflect a borrower's current risk profile, leading to mispriced loans. Protocols often use decentralized oracle networks and time-weighted averaging to mitigate this.

With less than 100% collateral, default risk is inherent. Enforcement is social and financial, not purely algorithmic:

• Reputation Slashing: Default leads to a permanent, on-record loss of credit score or Soulbound Token standing, harming future borrowing capacity.
• Social Guarantors: Some models involve co-signers whose reputation is also slashed upon default.
• Progressive Liquidation: Partial liquidation of the borrower's provided collateral, if any, and potential legal recourse frameworks in hybrid models.

Building a reputation score often requires exposing personal or financial data, creating privacy risks:

• On-chain permanence: Reputation data stored on a public blockchain is immutable and visible to all.
• Data correlation: Even pseudonymous data can be deanonymized when combined with other on-chain activity.
• Mitigations include using zero-knowledge proofs (ZKPs) to generate a credit score proof without revealing underlying data and storing sensitive data off-chain with selective disclosure.

Like all DeFi protocols, the underlying smart contracts are vulnerable to bugs and exploits. Unique considerations include:

• Complex logic: Reputation algorithms and oracle integrations increase contract complexity and attack surface.
• Governance attacks: If the protocol uses a governance token, an attacker could potentially manipulate reputation parameters or loan terms.
• Upgrade risks: The ability to upgrade contracts to fix bugs must be balanced with the risk of malicious upgrades. Rigorous audits and timelock mechanisms are essential.

Operating in a legal gray area creates significant compliance risk:

• Securities laws: Could the reputation token or loan agreement be classified as a security?
• Lending regulations: These loans may fall under traditional credit and usury laws, requiring licenses.
• Enforceability: The legal standing of reputation slashing or on-chain default records in court is untested.
• KYC/AML: Protocols may be forced to integrate Know Your Customer checks, conflicting with permissionless ideals.