Smarter Risks for Smart Contracts: Machine Learning Approach to Credit Scoring and Risk Assessment in DeFi

Decentralized finance (DeFi) represents a rapidly evolving domain of blockchain technology, offering a transformative approach to financial services through Web3 applications. By leveraging smart contracts, DeFi enables developers to create flexible and innovative financial solutions. However, the absence of standardized approaches introduces significant challenges for applying traditional financial models to risk assessment, particularly during the early stages of adoption. Among DeFi platforms, the Maker protocol stands out for its extensive functionalities, including loan services. This study aims to develop machine learning models for credit scoring at both the individual user level and for assessing platform-wide risk through the analysis of user default probabilities. Utilizing real data from the MakerDAO protocol, including user historical wallet activity and outputs from theoretical models, we conduct numerical experiments to evaluate the efficacy of these models in managing loan portfolio risks. The proposed approach enhances existing risk assessment methodologies by incorporating not only individual historical behaviors but also the potential for joint defaults. Additionally, it provides a robust framework for quantifying protocol-wide risk metrics, such as Value at Risk (VaR), offering deeper insights into the stability of the lending system. The findings underscore the necessity of creating tailored risk assessment models for specific DeFi projects and pave the way for further exploration of risk modeling in other DeFi protocols.