Deep Reputation Scoring in DeFi: zScore-Based Wallet Ranking from Liquidity and Trading Signals

TL;DR

This paper introduces a deep learning-based reputation scoring system for Uniswap users, combining rule-based blueprints and neural networks to differentiate liquidity providers from traders for improved risk assessment.

Contribution

It presents a novel framework that integrates rule-based behavioral blueprints with deep neural networks to generate context-aware reputation scores in DeFi.

Findings

Scores effectively differentiate user roles in Uniswap.

The system improves user segmentation and risk modeling.

Incorporates pool-level context for nuanced behavior analysis.

Abstract

As decentralized finance (DeFi) evolves, distinguishing between user behaviors - liquidity provision versus active trading - has become vital for risk modeling and on-chain reputation. We propose a behavioral scoring framework for Uniswap that assigns two complementary scores: a Liquidity Provision Score that assesses strategic liquidity contributions, and a Swap Behavior Score that reflects trading intent, volatility exposure, and discipline. The scores are constructed using rule-based blueprints that decompose behavior into volume, frequency, holding time, and withdrawal patterns. To handle edge cases and learn feature interactions, we introduce a deep residual neural network with densely connected skip blocks inspired by the U-Net architecture. We also incorporate pool-level context such as total value locked (TVL), fee tiers, and pool size, allowing the system to differentiate similar user behaviors across pools with varying characteristics. Our framework enables context-aware and scalable DeFi user scoring, supporting improved risk assessment and incentive design. Experiments on Uniswap v3 data show its usefulness for user segmentation and protocol-aligned reputation systems. Although we refer to our metric as zScore, it is independently developed and methodologically different from the cross-protocol system proposed by Udupi et al. Our focus is on role-specific behavioral modeling within Uniswap using blueprint logic and supervised learning.