Asset Risk
Overview
Asset Risk AI is responsible for continuously assessing the risk levels of collateral assets within TrenOS’s ecosystem. By analyzing liquidity depth, volatility, historical risk trends, and external risk assessments, this AI agent ensures that only secure and stable assets are supported within the protocol.
This AI agent eliminates the need for human-based risk evaluations, providing real-time risk scoring, collateral adjustments, and asset onboarding decisions. It ensures the protocol remains resilient against bad debt accumulation, flash loan exploits, and liquidity crises.
Function
Asset Risk AI evaluates the security and viability of assets used as collateral by:
Conducting real-time risk scoring for each collateral asset based on liquidity, volatility, and security parameters.
Dynamically adjusting loan-to-value (LTV) ratios and liquidation thresholds based on changing risk profiles.
Assessing whether new assets meet risk requirements for inclusion or if existing assets need delisting.
Detecting smart contract vulnerabilities, governance attacks, and flash loan threats that could impact asset safety.
How It Works
Analyzes liquidity conditions, market depth, and price volatility for each collateral asset.
Assesses security risks by scanning smart contracts, oracle reliability, and asset history.
Adjusts risk parameters, including LTV ratios and liquidation buffers, based on real-time data.
Updates risk classifications and governance reports to reflect changing asset security levels.
Goals
Ensure protocol safety by preventing high-risk assets from being used as collateral.
Improve capital efficiency by dynamically adjusting collateral requirements based on risk profiles.
Mitigate market volatility risks by preventing exposure to highly volatile or illiquid assets.
Automate risk governance by eliminating reliance on manual risk assessment teams.
Decision Logic
Step 1: Real-Time Market & Price Analysis
If an asset’s volatility exceeds 50% over 24 hours, increase collateral requirements.
If an asset’s liquidity depth falls below a critical threshold, reduce borrowing limits.
Step 2: Security & Smart Contract Risk Evaluation
If an asset is flagged in a security report (Immunefi, Forta, or audit alerts), it is placed under review.
If a smart contract update introduces new risks, borrowing is restricted until further analysis.
Step 3: Historical Liquidation & Risk Data Analysis
AI retrieves past liquidation events and governance risk reports to refine risk modeling.
If an asset has been historically associated with high default rates, it is reassessed.
Step 4: Execution
Adjusts collateral parameters in smart contracts.
Generates risk alerts and governance updates.
Input Data
Asset Risk AI relies on multiple sources of real-time and historical data to assess risk effectively:
Price & volatility metrics including asset price fluctuations, trading volume, and market depth.
Liquidity & TVL data to track capital flow and lending pool exposure.
On-chain risk indicators such as large asset movements, flash loan activity, and contract interactions.
Security & exploit reports from external audits and security platforms.
Historical liquidation records to assess past risk performance and adjust future risk models.
Execution Outputs
Collateral ratio updates to dynamically adjust LTV based on risk scores.
Delisting or listing of assets based on automated risk evaluations.
Borrowing limit modifications to prevent overexposure to high-risk assets.
Security alerts flagging assets showing signs of manipulation, high volatility, or liquidity crises.
Governance log updates to provide full transparency on AI-driven risk assessments.
Tools Used
Asset Risk AI integrates various tools to monitor, assess, and execute risk mitigation strategies:
API calls to fetch real-time asset pricing, trading volume, and liquidity depth.
Smart contract risk scanners to detect vulnerabilities and governance risks.
Historical risk analysis engines to compare current asset trends with past liquidation events.
Execution engine to update LTV ratios, borrowing caps, and risk classifications.
Governance risk alerts to flag potential threats and provide proactive responses.
RAG (Retrieval-Augmented Generation) to analyze past governance reports and liquidation records.
Security and Fail-Safes
To prevent market manipulation, incorrect risk assessments, or systemic failures, Asset Risk AI implements multiple security layers:
Multi-source data validation to cross-check risk scores across multiple oracle feeds and security reports.
Risk-based borrowing limits to prevent excessive leverage on high-volatility assets.
Emergency lock mechanisms that suspend borrowing on assets flagged as high-risk.
AI-driven historical risk memory to prevent reintroduction of previously delisted assets.
Governance fallback requiring human intervention for extreme risk scenarios.
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