Credit Scoring

CONFIDENTIAL & PROPRIETARY © 2025 Inkwell Finance, Inc. All Rights Reserved. This document is for informational purposes only and does not constitute legal, tax, or investment advice, nor an offer to sell or a solicitation to buy any security or other financial instrument. Any examples, structures, or flows described here are design intent only and may change.

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Overview

Leviathan’s credit scoring system, managed by the leviathan-score Solana program, provides on-chain credit assessments for borrowers. Every borrower has a score between 0 and 1000 that determines their borrowing terms, limits, and eligibility across the protocol. The system is designed around a key principle: scores live on-chain for transparency and composability, while the raw data and models that produce them remain off-chain for privacy and flexibility.

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How Scoring Works

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1. Data Collection

The scoring pipeline analyzes a borrower’s on-chain footprint:

• Trading history and portfolio composition
• Historical performance across markets and protocols
• Position sizing patterns and risk management behavior
• Repayment history on previous Leviathan loans

Data is collected from public on-chain sources. Borrowers are not required to submit documentation or undergo manual review — their on-chain track record speaks for itself.

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2. ML Analysis

An ensemble of machine learning models evaluates the collected data:

• Multiple models analyze different aspects of borrower behavior
• Model outputs are combined to produce a robust, well-calibrated score
• The ensemble approach reduces the impact of any single model’s weaknesses

The ML pipeline runs off-chain to maintain model privacy and computational flexibility. Specific model architectures and training data are not disclosed.

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3. Score Publication

Computed scores are published on-chain through oracle integration:

• Scores are written to per-borrower credit registry accounts on Solana
• Each score update includes a timestamp for freshness tracking
• Historical score data is preserved, enabling trend analysis

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4. Term Determination

On-chain scores directly affect borrowing parameters:

• Higher scores unlock better rates, higher limits, and longer terms
• Lower scores result in more conservative terms or ineligibility
• Score thresholds are enforced programmatically — there is no manual override

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Score Range and Tiers

Scores map to risk tiers that the protocol uses for term determination:

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Privacy Model

The credit scoring system balances transparency with privacy: On-chain (public):

• Credit score (0–1000)
• Risk tier classification
• Score timestamps and update history
• Loan performance history (repayments, defaults)

Off-chain (private):

• Raw trading data and transaction history
• ML model inputs and feature vectors
• Model architectures, weights, and training data
• Intermediate scoring calculations

This separation ensures that borrowers benefit from transparent, verifiable scores while their detailed financial behavior is not exposed on a public ledger.

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Oracle Integration

Scores are bridged from the off-chain ML pipeline to on-chain accounts through oracle feeds:

• Oracle operators submit score updates with cryptographic attestations
• The leviathan-score program validates attestations before accepting updates
• Stale scores (beyond a configurable age threshold) can be flagged or excluded from lending decisions

Oracle integration ensures that on-chain programs can trust the integrity of off-chain computations without needing to replicate the full scoring pipeline on-chain.

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Composability

Because credit scores live on-chain as standard Solana accounts, they are composable:

• Other programs can read a borrower’s score to make their own lending or risk decisions
• Scores can be used as inputs to automated strategies or governance mechanisms
• Third-party protocols can integrate Leviathan credit data without permission

This creates a shared credit layer that benefits the broader ecosystem, not just Leviathan’s own lending pools.