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Link Credit

AI-powered DeFi credit boosts that dynamically improve lending rates via Plaid + World ID.

DeFi & Tokenization CRE & AI Privacy Risk & Compliance World Tenderly

What it is

Link Credit is a credit layer for DeFi lending.
Today, most protocols require heavy overcollateralization because they cannot evaluate off-chain repayment behavior and cannot reliably
 prevent Sybil identity abuse.

 Link Credit combines three signals to solve this:
 1. Plaid cashflow data (user-permissioned, sandbox in demo)
 2. World ID proof-of-personhood (one human, one scoring identity)
 3. Chainlink CRE workflow execution to compute and deliver on-chain credit outcomes

 How it works:
 - User links bank data via Plaid and verifies identity via World ID.
 - A Chainlink CRE workflow exchanges Plaid tokens, fetches balances/transactions, computes a deterministic credit score, then applies bounded
  AI calibration.
 - The workflow writes the final score (scoreBps) on-chain.
 - Lending logic reads this score and applies a Plaid-based credit boost.
 - World ID verification adds a fixed +10% boost.
 - Final boost is additive: finalBoost = plaidBoost + worldIdBoost.

 This improves capital efficiency for good borrowers while preserving privacy and adding Sybil resistance.

How it Works

We built it as a simple end-to-end pipeline:
 - A React frontend where users connect wallet, run Plaid, verify with World ID, and borrow.
 - A lightweight API layer that creates Plaid link tokens and triggers workflows.
 - A Chainlink CRE workflow that does the heavy lifting: exchange Plaid token, fetch balances/transactions, run scoring, and write the final
  score on-chain.
 - Smart contracts (Aave-based integration + credit oracle) that read the score and apply borrowing boosts.

 The scoring model is practical: a deterministic rule-based score first, then a bounded AI adjustment for edge cases.
 So users get better lending terms from real financial behavior, while keeping the result transparent and controlled.

Links

Created by

  • HH
  • Noy Wang