OracleAI Nodes

The Credible OracleAI system leverages advanced decentralized oracle technology to transform how credit scoring, lending, and borrowing functions in the decentralized finance (DeFi) ecosystem. By utilizing AI-driven models, zero-knowledge (ZK) proofs, and a robust node architecture, Credible is enabling more accurate, transparent, and secure financial assessments and data analytics for wallets, platforms, and institutions alike. Below, we will explore how OracleAI is being used for anonymous wallet scoring, the technical infrastructure behind it, and the benefits it brings to both retail and institutional users.

OracleAI for Anonymous Wallet Scoring and Credit History

One of the key applications of OracleAI is its ability to provide an anonymous wallet scoring system, helping build an on-chain credit history for users. In traditional finance, credit scores are essential for assessing an individual’s creditworthiness, but this system is often absent in the decentralized world. Credible solves this problem by integrating various on-chain activities, such as lend/borrow operations, defaults, and loan repayments, to build a comprehensive and verifiable credit report for each wallet.

• Data Points for Credit Reports: OracleAI collects data on a range of wallet activities including:

  • Lend/borrow operations: Tracking loan origination, repayments, and interest paid.

  • Default and liquidation events: Monitoring defaults or forced liquidations, key indicators of a wallet’s risk profile.

  • On-time payments: Evaluating the wallet's ability to make timely payments and maintain solvency.

  • Cross-platform exposure: Examining interactions with other DeFi platforms, such as number of open positions, borrowed assets, and yield farming activities.

• ZK-based KYC Verification: OracleAI supports privacy-preserving ZK-KYC verification, where certain wallets can optionally verify their identity via zero-knowledge proofs without exposing sensitive data. This allows platforms and institutions to verify the credibility of users while preserving privacy, adding an extra layer of security and trust to the wallet’s credit score.

• Credit Reports for Underwriting and LTV Calculations: The wallet scores generated by OracleAI can be used for underwriting loans and calculating Loan-to-Value (LTV) ratios. This is crucial for both secured lending, where collateral is provided, and unsecured lending, where creditworthiness is the primary factor. The report serves as an important resource for credit assessment platforms, such as Credora, which rely on accurate data to generate ratings and risk assessments for lending pools.

In this way, OracleAI creates a seamless bridge between traditional credit models and the decentralized world, empowering both borrowers and lenders with better financial decision-making tools.

How OracleAI’s VRF-Based Node Architecture Ensures Data Accuracy and Availability

Credible OracleAI operates on a Verifiable Random Function (VRF)-based node architecture to maintain the integrity, accuracy, and availability of on-chain data used in credit assessments.

• How VRF Works:

  • VRF is a cryptographic proof mechanism that generates verifiable random outputs. In the context of OracleAI, VRF is used to ensure that the nodes providing data to the system are randomly selected, ensuring a decentralized and fair process for data validation.

  • This randomness prevents the potential manipulation of data or collusion among validators, as it guarantees that no single node or validator has control over the data that is sent to the OracleAI system.

• Data Fees and On-Chain Accuracy: OracleAI nodes interact with on-chain platforms, and their data is used to settle transactions or update credit scores in real-time. Since the data is being retrieved and verified from on-chain sources, it is inherently trustless and accurate. Nodes are incentivized to provide correct and reliable data via on-chain fees which users (such as lending platforms or credit assessment agencies) pay to access the data.

• Use by Other DeFi Protocols:

  • Lending platforms, such as Aave, Compound, and others, integrate OracleAI to adjust loan conditions based on wallet scores and credit history, offering better lending terms for high-scoring wallets.

  • Credit assessment agencies or platforms, such as Credora, can rely on the data generated by OracleAI to evaluate creditworthiness, set ratings, and manage risk profiles for individuals or institutional borrowers.

Through this decentralized data architecture, OracleAI ensures that the data it provides is tamper-proof, easily accessible, and can be trusted by users across the DeFi space.

Training the OracleAI Model: The Data Behind the Score

The OracleAI model is trained using a diverse set of data points gathered from users’ on-chain activities. As the system grows, it learns and improves, providing increasingly accurate credit scores over time. The training data includes:

• Lend and Borrow Activity: The volume, frequency, and consistency of loans taken and repaid are vital in determining creditworthiness.

• Defaults and Liquidations: Historical default data, along with liquidation events, provide insight into the risk profile of a wallet.

• On-time Payments: Wallets with a history of timely repayments are considered more reliable, and this data feeds directly into the model’s scoring mechanism.

• Investment and Open Positions: The number of active loans, investments, and open positions in decentralized exchanges (DEXs) offers valuable insight into a wallet’s financial habits and risk appetite.

• Wallet Age and Balance: Older wallets with consistent balances are generally seen as more reliable, so the model uses wallet age and balance averages to refine its credit assessment.

• Exposure to High-Risk Wallets or Platforms: Identifying whether a wallet has interacted with high-risk platforms or addresses is crucial for assessing the wallet’s overall risk level.

The AI model continuously improves as it processes more data, ensuring that wallet scores and credit reports are always up to date with the latest financial behavior.

Distributed Node Architecture: Ensuring Scalability, Reliability, and Perks for License Holders

Credible’s distributed node architecture ensures the scalability and reliability of OracleAI’s credit scoring and data validation system. This decentralized approach offers significant benefits:

• 100,000 Nodes in 120 Tiers: The network consists of 100,000 nodes, divided into 120 tiers. Each tier represents different levels of access and node responsibilities, ensuring that data is processed quickly and accurately while also preventing network congestion.

• Non-Transferrable Node Licenses: The node licenses are non-transferrable, meaning they are uniquely assigned to the buyer. This ensures that only the original license holder can use or assign the node to a NodeOps (node operator), who manages the node on their behalf.

• $CRED and Perks: Node license holders can earn $CRED tokens as rewards for operating their nodes. In addition, node operators can access various token-gated perks within the Credible ecosystem, such as access to exclusive data, advanced credit assessments, and governance voting rights on protocol decisions.

• Alien Art PFP for Licenses: Each node license is uniquely represented by an Alien Art Profile Picture (PFP), which is randomly generated using AI. This PFP not only serves as a digital asset but also acts as a symbol of ownership for the node license, adding an artistic and gamified layer to the process.

This distributed node system ensures that OracleAI remains decentralized, available, and scalable, while also providing unique economic incentives and community engagement opportunities for license holders.