On-Chain Credit Evaluation Models and Economic Incentive Mechanism Optimization for Decentralized Finance (DeFi)

Abstract

With the development of Decentralized Finance (DeFi), on-chain credit evaluation has become crucial for ensuring the security of financial transactions. However, most decentralized platforms currently face shortcomings in credit evaluation and incentive mechanism design, particularly lacking effective credit evaluation models and incentive mechanisms to enhance user participation and system efficiency. While blockchain-based credit evaluation methods have been explored, existing research primarily focuses on data privacy and security, neglecting the optimization of economic incentive mechanisms. This paper proposes a blockchain-based on-chain credit evaluation model and introduces a novel method for optimizing economic incentive mechanisms. The model constructs a multi-level credit scoring system through smart contracts and decentralized data flow mechanisms, while applying game theory to optimize the incentive mechanism to improve system participation and stability. Experimental results show that the proposed method achieves an accuracy of 85.2% on a self-built DeFi dataset, an improvement of approximately 1.8 percentage points over the current state-of-the-art model (SOTA Method 2). Additionally, the inference time is reduced to 100 milliseconds, achieving a 50% improvement in efficiency. This research provides a new credit evaluation model and incentive mechanism optimization approach for the DeFi field, enriches the application research of blockchain technology in finance, and offers practical guidance for the design of decentralized platforms.