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Blockchain technology has revolutionized the financial sector by introducing decentralized finance (DeFi) as a powerful alternative to traditional banking systems. Among DeFi sectors, lending has become a key area, facilitating cryptocurrency borrowing and lending without intermediaries. As of May 2024, lending ranks second in total value locked (TVL) within DeFi, reflecting its widespread adoption. Key entities in the lending ecosystem, including personal wallets, centralized exchange, lending smart contracts, and protocol supported tokens, play a crucial role in shaping governance, decision-making, and resource allocation. However, current evaluation methods, which primarily rely on token holdings for governance voting, are vulnerable to manipulation and fail to accurately reflect contributions. To address this, we propose a novel scoring framework that evaluates entities based on both token holdings and lending interactions over time. Our framework uses the PageRank algorithm, scaled to the FICO (Fair Isaac Corporation) score range to offer a more stable and transparent assessment. This approach promotes healthy competition, encourages user activity, and supports the long-term growth and stability of the lending DeFi ecosystem.

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A Reputation Scoring Framework for Lending Protocols Using the PageRank Algorithm

  • 会议论文

  • 发表日期：

    2025年

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  • Mau-Tra Nguyen,
  • Tuan-Dat Trinh,
  • Viet-Bang Pham

摘要

Blockchain technology has revolutionized the financial sector by introducing decentralized finance (DeFi) as a powerful alternative to traditional banking systems. Among DeFi sectors, lending has become a key area, facilitating cryptocurrency borrowing and lending without intermediaries. As of May 2024, lending ranks second in total value locked (TVL) within DeFi, reflecting its widespread adoption. Key entities in the lending ecosystem, including personal wallets, centralized exchange, lending smart contracts, and protocol supported tokens, play a crucial role in shaping governance, decision-making, and resource allocation. However, current evaluation methods, which primarily rely on token holdings for governance voting, are vulnerable to manipulation and fail to accurately reflect contributions. To address this, we propose a novel scoring framework that evaluates entities based on both token holdings and lending interactions over time. Our framework uses the PageRank algorithm, scaled to the FICO (Fair Isaac Corporation) score range to offer a more stable and transparent assessment. This approach promotes healthy competition, encourages user activity, and supports the long-term growth and stability of the lending DeFi ecosystem.