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

arXiv: https://arxiv.org/abs/2507.20494 · HTML: https://mlsanity.com/paper/2507.20494/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 21 references that are in the map.
- First posted on arXiv 28 Jul 2025.

## Builds on (most cited first, 4 of 4)

- [Transparency, Auditability and eXplainability of Machine Learning Models in Credit Scoring](https://mlsanity.com/paper/2009.13384/) (2020)
- [Machine Learning approach for Credit Scoring](https://mlsanity.com/paper/2008.01687/) (2020)
- [zScore: A Universal Decentralised Reputation System for the Blockchain Economy](https://mlsanity.com/paper/2503.05718/) (2025)
- [RiskProp: Account Risk Rating on Ethereum via De-anonymous Score and Network Propagation](https://mlsanity.com/paper/2301.00354/) (2023)

## Built on by (most cited first, 3 of 3)

- [ZAPs: A Reward Attribution Framework for DeFi Ecosystems with Adversarial-Robust Scoring via Parallel Anomaly Ensemble Detection](https://mlsanity.com/paper/2607.27859/) (2026)
- [zLend: A Dual-Scope Cash-Flow Reconstruction Framework for On-Chain Credit Underwriting](https://mlsanity.com/paper/2608.16856/) (2026)
- [zScore-N: A Neural Network for On-Chain Wallet Reputation Scoring](https://mlsanity.com/paper/2609.08247/) (2026)

## Abstract (arXiv)

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.