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We propose a survival analysis approach for discovering and characterizing user behavior and risks for lending protocols in decentralized finance (DeFi). We demonstrate how to gather and prepare DeFi transaction data for survival analysis. We illustrate our approach using transactions in Aave, one of the largest lending protocols. We develop a DeFi survival analysis pipeline that first prepares transaction data for survival analysis through the selection of different index events (or transactions) and associated outcome events. Then we apply survival analysis statistical and visualization methods modified for competing risks when appropriate, such as Kaplan–Meier survival curves, cumulative incidence functions, Cox hazard regression, and Fine-Gray models for sub-distribution hazards to gain insights into usage patterns and risks within the protocol. We show how, by varying the index and outcome events as well as covariates, we can use DeFi survival analysis to answer questions like “How does loan size affect the repayment schedule of the loan?”; “How does loan size affect the likelihood that an account gets liquidated?”; “How does user behavior vary between Aave markets?”; “How has user behavior in Aave varied from quarter to quarter?” The proposed DeFi survival analysis can easily be generalized to other DeFi lending protocols. By defining appropriate index and outcome events, DeFi survival analysis can be applied to any cryptocurrency protocol with transactions. 

The emerging decentralized financial ecosystem (DeFi) is comprised of numerous protocols, one type being lending protocols. People make transactions in lending protocols, each of which is attributed to a specific blockchain address which could represent an externally-owned account (EOA) or a smart contract. Using Aave, one of the largest lending protocols, we summarize the transactions made by each address in each quarter from January 1, 2021, through December 31, 2022. We cluster these quarterly summaries to identify and name common patterns of quarterly behavior in Aave. We then use these clusters to glean insights into the dominant behaviors in Aave. We show that there are three kinds of keepers, i.e., a specific type of users tasked with the protocol’s governance, but only one kind of keeper finds consistent success in making profits from liquidations. We identify the largest-scale accounts in Aave and the highest-risk kinds of behavior on the platform. Additionally, we use the temporal aspect of the clusters to track how common behaviors change through time and how usage has shifted in the wake of major events that impacted the crypto market, and we show that there seem to be problems with user retention in Aave as many of the addresses that perform transactions do not remain in the market for long.