DeFi Survival Analysis: Insights into Risks and User Behaviors

Aaron Green (), Christopher Cammilleri (), John S. Erickson (), Oshani Seneviratne () and Kristin P. Bennett ()
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Aaron Green: Rensselaer Polytechnic Institute
Christopher Cammilleri: Rensselaer Polytechnic Institute
John S. Erickson: Rensselaer Polytechnic Institute
Oshani Seneviratne: Rensselaer Polytechnic Institute
Kristin P. Bennett: Rensselaer Polytechnic Institute

A chapter in Mathematical Research for Blockchain Economy, 2023, pp 127-141 from Springer

Abstract: Abstract We propose a decentralized finance (DeFi) survival analysis approach for discovering and characterizing user behavior and risks in lending protocols. We demonstrate how to gather and prepare DeFi transaction data for survival analysis. We demonstrate our approach using transactions in AAVE, one of the largest lending protocols. We develop a DeFi survival analysis pipeline which 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 such as median survival times, Kaplan–Meier survival curves, and Cox hazard regression to gain insights into usage patterns and risks within the protocol. We show how by varying the index and outcome events, we can utilize DeFi survival analysis to answer three different questions. What do users do after a deposit? How long until borrows are first repaid or liquidated? How does coin type influence liquidation risk? 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.

Date: 2023
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DOI: 10.1007/978-3-031-18679-0_8

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