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

arXiv: https://arxiv.org/abs/2604.14583 · HTML: https://mlsanity.com/paper/2604.14583/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 32 references that are in the map.
- First posted on arXiv 16 Apr 2026.

## Builds on (most cited first, 5 of 5)

- [An Empirical Study of DeFi Liquidations: Incentives, Risks, and Instabilities](https://mlsanity.com/paper/2106.06389/) (2021)
- [CeFi vs. DeFi -- Comparing Centralized to Decentralized Finance](https://mlsanity.com/paper/2106.08157/) (2021)
- [Enabling Cross-Language Data Integration and Scalable Analytics in Decentralized Finance](https://mlsanity.com/paper/2311.02272/) (2023)
- [FinSurvival: A Suite of Large Scale Survival Modeling Tasks from Finance](https://mlsanity.com/paper/2507.14160/) (2025)
- [Benchmarking Temporal Web3 Intelligence: Lessons from the FinSurvival 2025 Challenge](https://mlsanity.com/paper/2602.23159/) (2026)

## Abstract (arXiv)

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.