From Risk to Rescue: An Agentic Survival Analysis Framework for Liquidation Prevention

2026-04-15

AI Summary

This study addresses the challenge of erroneous liquidations in decentralized finance (DeFi) caused by market volatility, which existing risk management tools—relying on static health factors—fail to distinguish from benign “dust” events and cannot proactively mitigate. To overcome these limitations, this work proposes the first active DeFi risk control framework integrating survival analysis with autonomous agents. It employs a numerically stable XGBoost Cox model to predict liquidation risk, leverages a high-fidelity Aave v3 simulator coupled with a counterfactual optimization loop to generate minimal-cost intervention strategies, and executes them via protocol-compatible mechanisms. Evaluated on 4,882 high-risk users, the approach precisely identifies actionable risks, entirely prevents false liquidations, achieves capital-efficient outcomes without exacerbating risk, and introduces novel metrics including normalized payback period and volatility-adjusted trend scores.

Technology Category

• Multiagent Systems: Mechanism Design
• Reasoning under Uncertainty: Sequential Decision Making
• Machine Learning: Active Learning

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Decentralized Finance (DeFi) lending protocols like Aave v3 rely on over-collateralization to secure loans, yet users frequently face liquidation due to volatile market conditions. Existing risk management tools utilize static health-factor thresholds, which are reactive and fail to distinguish between administrative "dust" cleanup and genuine insolvency. In this work, we propose an autonomous agent that leverages time-to-event (survival) analysis and moves beyond prediction to execution. Unlike passive risk signals, this agent perceives risk, simulates counterfactual futures, and executes protocol-faithful interventions to proactively prevent liquidations. We introduce a return period metric derived from a numerically stable XGBoost Cox proportional hazards model to normalize risk across transaction types, coupled with a volatility-adjusted trend score to filter transient market noise. To select optimal interventions, we implement a counterfactual optimization loop that simulates potential user actions to find the minimum capital required to mitigate risk. We validate our approach using a high-fidelity, protocol-faithful Aave v3 simulator on a cohort of 4,882 high-risk user profiles. The results demonstrate the agent's ability to prevent liquidations in imminent-risk scenarios where static rules fail, effectively "saving the unsavable" while maintaining a zero worsening rate, providing a critical safety guarantee often missing in autonomous financial agents. Furthermore, the system successfully differentiates between actionable financial risks and negligible dust events, optimizing capital efficiency where static rules fail.

Problem

• DeFi liquidation
• risk management
• over-collateralization
• health factor
• market volatility

Innovation

• survival analysis
• autonomous agent
• liquidation prevention
• counterfactual optimization
• DeFi risk management