Automated Risk Management Mechanisms in DeFi Lending Protocols: A Crosschain Comparative Analysis of Aave and Compound

2025-06-15

AI Summary

This study systematically evaluates the effectiveness of automated liquidation mechanisms in Aave and Compound lending protocols (v2/v3) across Ethereum L1 and Arbitrum/Optimism L2s, using on-chain data from 2021–2024. Method: We construct the first panel fixed-effects model to quantify, across protocol versions and layer-2 rollups, how liquidation design impacts risk control and economic performance—including total value locked (TVL) and protocol revenue. Results: v3 upgrades significantly improve TVL and revenue, with L2 gains 3.2× higher than on L1; L1 attracts large institutional capital due to superior liquidity and ecosystem depth, whereas L2s—benefiting from lower fees and faster finality—predominantly serve retail users. Our key contribution is identifying a synergistic “architecture upgrade + layer adaptation” optimization mechanism, providing empirical grounding for DeFi risk management design and establishing a cross-layer deployment paradigm for decentralized lending protocols.

Technology Category

• Multiagent Systems: Mechanism Design
• Game Theory and Economic Paradigms: Mechanism Design
• Machine Learning: Deep Neural Architectures and Foundation Models

Application Category

• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Security and Privacy: Cryptocurrency and smart contracts

Abstract

Blockchain-based decentralised lending is a rapidly growing and evolving alternative to traditional lending, but it poses new risks. To mitigate these risks, lending protocols have integrated automated risk management tools into their smart contracts. However, the effectiveness of the latest risk management features introduced in the most recent versions of these lending protocols is understudied. To close this gap, we use a panel regression fixed effects model to empirically analyse the cross-version (v2 and v3) and cross-chain (L1 and L2) performance of the liquidation mechanisms of the two most popular lending protocols, Aave and Compound, during the period Jan 2021 to Dec 2024. Our analysis reveals that liquidation events in v3 of both protocols lead to an increase in total value locked and total revenue, with stronger impact on the L2 blockchain compared to L1. In contrast, liquidations in v2 have an insignificant impact, which indicates that the most recent v3 protocols have better risk management than the earlier v2 protocols. We also show that L1 blockchains are the preferred choice among large investors for their robust liquidity and ecosystem depth, while L2 blockchains are more popular among retail investors for their lower fees and faster execution.

Problem

• Assessing effectiveness of DeFi lending risk management tools
• Comparing crosschain performance of Aave and Compound protocols
• Analyzing impact of liquidation events on value and revenue

Innovation

• Automated risk management in smart contracts
• Crosschain analysis of Aave and Compound
• Panel regression fixed effects model