Lending System

HyperTrend offers innovative on-chain lending services, leveraging credit scoring to enable uncollateralized or lightly collateralized loans, allowing users to efficiently access funds for trading.

Core Features

Credit-Based Lending

• Scoring-Driven Matching: Loan limits, interest rates, and collateral ratios are tailored based on the credit model score.

• Tiered Pricing:

  • High-credit users: Higher limits, lower rates, and reduced or zero collateral.

  • Low-credit users: Limited amounts, elevated rates, and light collateral requirements.

High Capital Efficiency

• Eliminates over-collateralization, improving capital utilization by over 200% compared to traditional DeFi models.

Smart Contract Automation

• All lending operations — from credit evaluation and loan issuance to repayment and liquidation — are executed by autonomous smart contracts.

• Full transparency and immutability ensure trustless operations.

• The system automatically monitors repayment schedules and preemptively triggers protective actions when risk thresholds are breached.

Trading Integration

• Borrowed funds are directly connected to the Hyperliquid trading infrastructure.

• Users can instantly execute spot or perpetual trades without transferring assets across protocols.

• Enables seamless “Lend-to-Trade” integration for maximum capital efficiency.

Multi-Pool Architecture

• User-Created Pools: Any participant can establish customized lending pools.

• Configurable Parameters: Liquidity providers define rates, sizes, and governance roles.

• NFT-Based Management: Pool managers are identified via NFT ownership to ensure accountability.

• Decentralized Decision-Making: Reduces systemic risk through distributed governance.

Risk Management Framework

Traditional DeFi protocols rely on over-collateralization to absorb losses. HyperTrend replaces this with a multi-layer credit protection model designed to make bad debts controllable, predictable, and absorbable.

Dynamic Collateral Ratio

Credit Score	Collateral Ratio
>900	0% (No Collateral)
800-900	30%
700-800	60%
600-700	90%
<600	Ineligible

High-credit users enjoy unsecured loans, while low-credit users provide proportional collateral for risk balance.

Five-Layer Defense Mechanism

Layer	Name	Objective	Method
L1	Pre-Loan Screening	Prevent bad debt entry	Multi-dimensional credit evaluation
L2	Adaptive Limit Control	Cap high-risk exposure	Dynamic loan limit = f(score × risk weight)
L3	Real-Time Monitoring	Detect early signals	Score fluctuation & behavioral anomaly tracking
L4	Credit Liquidation	Automate recovery	Smart-contract-based liquidation
L5	Loss Absorption Layer	Buffer impacts	Reserve vault & DAO-backed compensation

Anti-Exploitation Mechanisms

Tranche Pool Model

Three-layer liquidity pools distribute risk progressively:

Level	Risk	Yield	Role
Senior Pool	Lowest	Stable interest	Conservative LPs (insured).
Mezzanine Pool	Medium	Floating returns	Standard LPs (partial risk sharing).
Junior Pool	Highest	High yields + token rewards	Risk-tolerant LPs/DAO members.

Losses are absorbed bottom-up, maintaining systemic stability.

Dynamic Social Collateral

Building on the social credit system, a dynamic weighting penalty is applied: High-credit users bear greater network liability, incentivizing selective invitations via "social game theory."

$$\text{Risk Allocation Ratio} = \frac{S_{\text{inviter}}}{\sum S_{\text{network}}}$$

Where $S_{\text{inviter}}$ ​ is the inviter's score, and $\sum S_{\text{network}}$ ​ sums scores in the invitation chain. This enables credit-weighted risk distribution, promoting self-governance.

Dynamic Credit Limit

To curb over-borrowing by mid-low credit users, a non-linear function caps loan amounts:

$$L_i = L_{\max} \cdot \left( \frac{S_i}{S_{\max}} \right)^{\alpha}$$

Where $L_i$ is the borrowable amount, $L_{\max}$ is the system max, $S_i$ is the score, $S_{\max}$ is the max score, and $\alpha > 1$ amplifies growth for high scores. This prevents "mid-low credit high-leverage overdrafts."

Reserve Vault Compensation

Upon bad debt detection, losses are calculated and compensated from the insurance pool:

$$L_{\text{loss}} = L_i - R_i$$

$$F_{\text{comp}} = \min(F_{\text{pool}}, L_{\text{loss}})$$

Where $L_{\text{loss}}$ is the net loss, $R_i$ is recovered funds, $F_{\text{pool}}$ is the fund balance, and $F_{\text{comp}}$ is the compensation. Events are logged for DAO audits, ensuring long-term pool health.

Default Blacklist and Reputation Penalties

For malicious defaults or non-repayment, on-chain penalties activate:

• Auto-mints a Default SBT (soul-bound token) to the user's DID for permanent record.

• Freezes credit scores, blocking recovery via trades or networks.

• Bans new accounts (via behavioral entropy and network tracking to counter Sybil revivals).

• Recovery via debt repayment + community reputation tasks.

Systemic Summary

The anti-exploitation framework integrates layered risk, dynamic accountability, automated compensation, and reputation enforcement, forming a self-healing credit economy where risks are continuously identified, absorbed, and converted into long-term trust.

Lending System Value

For Users:

• Access to credit-based, low-collateral liquidity.

• Instant capital deployment into trading for higher efficiency.

For the Platform:

• Real-time credit-driven risk monitoring.

• Reduced default ratio and enhanced liquidity resilience.

• Shifts DeFi lending from asset collateralization toward reputation-based trust finance.