AI-Driven Dynamic Collateralization in DeFi Lending: A Machine Learning Approach to Mitigating Liquidation Risks

Abstract

Decentralized Finance (DeFi) lending protocols currently rely on fixed collateralization ratios, leading to inefficiencies such as over-collateralization, frequent liquidations, and suboptimal capital utilization. This paper proposes a novel framework integrating machine learning (ML) with DeFi lending protocols to dynamically adjust collateral requirements in realtime based on borrower behavior, market volatility, and on-chain data. By analyzing historical loan performance, social sentiment, and macroeconomic indicators, the ML model optimizes collateral ratios to minimize liquidations while maintaining protocol security. We simulate the model using data from major DeFi platforms (e.g., Aave, Compound) and demonstrate a 30-50