Setting Up AI-Based Credit Scoring for Undercollateralized Loans

Undercollateralized lending is a frontier in DeFi that requires robust credit risk assessment. Traditional overcollateralized models, where a user must lock $150 to borrow $100, are capital-inefficient. AI-based credit scoring introduces a data-driven approach to evaluate a borrower's likelihood of repayment, enabling protocols like Goldfinch and TrueFi to offer loans with little or no upfront collateral. The core challenge is creating a trustless, transparent, and tamper-proof scoring system that operates on-chain, using verifiable data sources to predict creditworthiness without centralized intermediaries.

The first step is defining and sourcing the data for your model. On-chain data is inherently transparent and includes wallet transaction history, DeFi interaction patterns (e.g., liquidity provision, borrowing history), asset holdings, and on-chain identity attestations (like ENS names or Proof of Humanity). Off-chain data, such as traditional credit scores or bank statements, requires a privacy-preserving oracle solution like Chainlink Functions or DECO to bring verified claims on-chain without exposing raw data. The model's predictive power depends heavily on the quality and relevance of these input features.

Next, you must design and train the machine learning model. Common approaches include logistic regression, gradient-boosted trees (e.g., XGBoost), or neural networks. The model is trained off-chain on historical data to find patterns correlating user attributes with repayment outcomes. A critical step is model verifiability: the final trained model (its weights and architecture) must be published or its hash stored on-chain. This allows anyone to verify that the on-chain scoring function is executing the exact model that was audited, preventing manipulation of the scoring logic post-deployment.

Deploying the model for on-chain inference is the core technical challenge. For simpler models, you can implement the scoring logic directly in a smart contract. For complex models, you need a scalable compute solution. Ethereum Attestation Service (EAS) can be used to post verifiable attestations of credit scores. Alternatively, co-processor networks like Axiom or Brevis allow you to prove off-chain computation (like running an ML model) and submit a zero-knowledge proof (ZKP) of the result on-chain, ensuring correctness without re-executing the heavy computation in the EVM.

A practical implementation involves a smart contract that requests a score. For example, a CreditScoring contract could call an oracle with a user's wallet address. The oracle fetches the pre-processed feature vector, runs it through the verifiable model, and returns a score (e.g., a number from 300 to 850) and a risk premium. The lending protocol's LoanManager contract would then use this score to determine loan terms: a high score might grant a 0% collateral requirement with a 5% APY, while a lower score might require 25% collateral at a 15% APY. This logic is enforced immutably by the smart contract.

Key considerations for production include model decay (periodic retraining with new data), adversarial robustness against users trying to game the system, and regulatory compliance regarding fair lending. Successful integration transforms capital efficiency, allowing DeFi to serve a broader market. By combining transparent on-chain data, verifiable ML, and smart contract automation, developers can build the foundational layer for a more accessible and efficient global credit system.

The core of the system is a machine learning model that predicts a borrower's creditworthiness. You'll need proficiency in Python and libraries like scikit-learn, XGBoost, or TensorFlow/PyTorch for model development. For handling on-chain data, familiarity with web3.py or ethers.js is essential to query wallet histories, transaction patterns, and DeFi interactions from nodes or indexers like The Graph. Off-chain data, such as verified social or financial credentials, may require integration with oracle networks like Chainlink.

Smart contract development is required to operationalize the score. You'll write contracts in Solidity (for EVM chains) or Rust (for Solana) to receive score inputs, manage loan terms, and execute agreements. Use development frameworks like Hardhat or Foundry for testing and deployment. A critical component is a verifiable computation system, such as EigenLayer's AVS or a zk-rollup, to prove the model's inference was executed correctly off-chain without revealing the proprietary model itself.

Data sourcing and storage present significant challenges. You must identify and aggregate on-chain data signals: transaction frequency, NFT holdings, governance token participation, and repayment history from protocols like Aave or Compound. For a holistic view, you may incorporate off-chain data via privacy-preserving techniques like zero-knowledge proofs (ZKPs) from platforms like Sismo or zkPass. This data often needs to be stored and accessed via decentralized storage solutions like IPFS or Arweave for auditability.

Finally, consider the deployment architecture. A common pattern involves an off-chain scoring server (built with Node.js or Python) that pulls data, runs the model, and submits scores with proofs to the blockchain. You'll need to manage private keys securely for transaction signing, often using services like AWS KMS or GCP Secret Manager in development. For production, a decentralized network of node operators, potentially managed through a DAO or a service like API3's dAPIs, can enhance reliability and censorship resistance.

On-chain data for credit scoring extends far beyond simple wallet balances. To assess a borrower's financial behavior and reliability, you must aggregate and analyze a comprehensive dataset. This includes transaction history (frequency, volume, counterparties), DeFi interaction patterns (liquidity provision, borrowing, staking), asset composition (NFT holdings, token diversity), and on-chain identity signals (ENS names, POAPs, governance participation). Sourcing this data requires interacting with blockchain nodes or using specialized indexers and APIs from providers like The Graph, Covalent, or Dune Analytics.

Raw transaction logs are not directly usable by machine learning models. Feature engineering is the process of transforming this raw data into quantifiable, predictive signals. For example, instead of a raw list of transactions, you create features like avg_transaction_value_30d, unique_protocol_interactions, gas_spent_ratio, or time_since_first_tx. A crucial feature for undercollateralized lending is wallet profitability: calculating the net gain or loss from a user's DeFi activities across lending, swapping, and yield farming, which requires reconstructing their financial position from event logs.

For developers, this process begins by defining data pipelines. Using a service like The Graph, you write a subgraph to index specific events from relevant smart contracts. A simplified example to track a user's borrowing history from an Aave-like contract might listen for the Borrow event:

graphqlCopy
entity BorrowEvent {
  id: ID!
  user: Bytes! # user address
  reserve: Bytes! # asset address
  amount: BigInt!
  timestamp: BigInt!
}

This structured data is then aggregated into time-windowed features for your model.

Data quality and temporal consistency are paramount. You must handle challenges like wallet abstraction (users with multiple addresses), testnet activity, and sybil attacks. A robust pipeline includes address clustering (linking addresses owned by the same entity via funding paths or smart contract usage) and feature normalization (scaling values to account for different asset decimals and price volatility). The goal is to create a longitudinal profile that reflects a user's consistent financial behavior, not just a snapshot.

Finally, this engineered feature set forms the input layer for your machine learning model. Each feature should be tested for predictive power regarding loan repayment. Common techniques include analyzing feature importance scores from tree-based models like XGBoost or calculating statistical correlations with default events in historical datasets. The output of this step is a clean, labeled dataset ready for model training in Step 2, turning blockchain footprints into a quantifiable credit reputation.

The foundation of an AI-based credit scoring system is the predictive model. For undercollateralized lending, you typically train a supervised learning model, such as a gradient boosting machine (e.g., XGBoost, LightGBM) or a neural network, on historical loan performance data. The target variable is binary: 1 for a loan that was repaid and 0 for a default. Features are derived from the borrower's on-chain history (e.g., transaction frequency, gas spent, NFT holdings, DeFi interactions) and, if available, off-chain attestations. The model learns the complex, non-linear relationships between these features and the likelihood of default.

Training a model on sensitive financial data introduces significant privacy risks. Storing raw, identifiable user data on-chain or in a centralized database creates a single point of failure and violates user trust. To mitigate this, you must adopt privacy-enhancing technologies (PETs). A primary method is federated learning, where the model is trained across decentralized devices or nodes holding local data samples, without exchanging the raw data itself. Another approach is to use homomorphic encryption for computations on encrypted data, or zero-knowledge proofs (ZKPs) to verify a credit score without revealing the underlying inputs.

For on-chain integration, a common pattern is a two-step verification process. First, the model runs off-chain in a trusted execution environment (TEE) or via a federated learning framework. It outputs a credit score and, crucially, a ZK-SNARK proof attesting that the score was computed correctly according to the published model weights and the user's private inputs. Only this proof and the resulting score (often a hash of it) are submitted on-chain. The lending smart contract can then verify the proof in constant time, enabling permissionless loan approval without exposing the user's personal financial history to the public ledger.

Implementing this requires careful architecture. Your tech stack might involve: PySyft or TensorFlow Federated for federated learning prototypes, Zokrates or Circom for crafting ZKP circuits for model inference, and a blockchain like Ethereum or a dedicated app-chain for settlement. The model must be regularly retrained and audited to prevent model drift—where its predictions become less accurate as market behavior changes—and to ensure it does not introduce unintended bias against certain wallet activity patterns.

After training and validating your credit scoring model off-chain, the next step is to make its predictions available on-chain. This is achieved through on-chain inference, where the model's logic is executed within a smart contract or a specialized oracle. For complex models, a common pattern is to use a verifiable computation oracle like Giza or EZKL. These services generate a cryptographic proof (often a ZK-SNARK) that a specific input produced a given prediction, allowing the smart contract to trust the result without re-executing the entire model, which would be prohibitively expensive in gas.

The core integration involves a smart contract, typically the LendingPool, that requests a credit score before approving a loan. A basic flow involves: 1) The user submits a loan application with their wallet address and off-chain data identifiers. 2) An off-chain relayer (or the user) triggers the oracle to compute the score for that data. 3) The oracle returns the score and proof to the contract. 4) The contract verifies the proof and, if the score meets a predefined threshold, approves the loan. This keeps sensitive raw user data off-chain while bringing the trustless decision on-chain.

Here is a simplified snippet of a smart contract function that could receive and verify a score. This example assumes a hypothetical oracle that passes a pre-verified score and a signature.

solidityCopy
function requestLoan(uint256 requestedAmount, bytes32 dataHash) external {
    require(loans[msg.sender].amount == 0, "Existing loan");
    // In practice, an oracle would call this function with the score
    _evaluateApplication(msg.sender, requestedAmount, dataHash);
}

function _evaluateApplication(address applicant, uint256 amount, bytes32 dataHash) internal {
    // This would be called by a trusted oracle with a signed message
    uint256 creditScore = _fetchVerifiedScore(applicant, dataHash);
    require(creditScore >= MINIMUM_SCORE, "Insufficient credit score");
    
    // Calculate loan terms based on score (e.g., dynamic LTV)
    uint256 ltvRatio = baseLTV + (creditScore / SCORE_DIVISOR);
    uint256 maxLoan = (collateralValue * ltvRatio) / 100;
    require(amount <= maxLoan, "Amount exceeds limit for score");
    
    _createLoan(applicant, amount);
}

Key design considerations for integration include gas efficiency and latency. Verifying a ZK proof on-chain can cost 300k-1M+ gas, so it may be batched for multiple users or used only for larger loans. The update frequency of the model is also critical; a model can be retrained off-chain weekly, but updating its on-chain representation (e.g., new circuit or contract) requires a governance process. Furthermore, you must decide on a fallback mechanism for oracle failure, such as pausing new loans or using a committee of nodes for redundancy.

Finally, thorough testing is essential. Use a forked mainnet environment (like Foundry's forge) to simulate the full flow: user request, off-chain proof generation, on-chain verification, and loan issuance. Test edge cases such as invalid proofs, score boundary conditions, and oracle downtime. This integration layer is where the trustless promise of DeFi meets the predictive power of AI, enabling a new class of undercollateralized financial products.

The primary advantage of this architecture is its composability. Your CreditScoringOracle contract can be integrated into any lending protocol, such as Aave or Compound, by calling its getCreditScore function. The system's security hinges on the integrity of the off-chain data pipeline and the trustworthiness of the oracle signer. For production use, consider implementing a decentralized oracle network like Chainlink Functions or Pyth to fetch and attest to model scores, moving away from a single trusted signer.

To improve your model, explore additional on-chain data sources. Transaction history from The Graph, NFT ownership patterns, and governance participation (e.g., voting on Snapshot) can provide stronger signals of user reliability. You can also implement a feedback loop where loan repayment performance is recorded on-chain and used to retrain and improve the AI model off-chain, creating a self-reinforcing system.

Next, consider the regulatory and privacy implications. Using personal data for credit assessment may fall under jurisdictions like GDPR or CCPA. Explore zero-knowledge proofs (ZKPs) using frameworks like Circom or Noir to allow users to prove they have a sufficient credit score without revealing the underlying data, aligning with privacy-preserving principles.

For further development, audit your smart contracts thoroughly. The oracle and loan manager contracts handle financial logic and are prime targets. Use tools like Slither or Mythril for automated analysis and consider a professional audit from firms like OpenZeppelin or Trail of Bits before a mainnet deployment. Also, implement upgradeability patterns, such as a Transparent Proxy, to allow for model improvements without migrating user positions.

Finally, to see a complete reference implementation, examine projects like Goldfinch (trust-based consensus) or Maple Finance (delegated underwriter model). While not purely AI-driven, their structures for assessing borrower credibility provide a valuable blueprint. Continue experimenting on testnets, starting with a whitelist of known borrowers, and gradually decentralize the scoring mechanism as the system proves robust.