[论文] zLend: A Dual-Scope Cash-Flow Reconstruction Framework for On-Chain Cr...

论文概要

研究领域: ML 作者: Girish G N, Ashutosh Sahoo, Akshay SP et al. (5 authors) 发布时间: 2026-08-17 arXiv: 2608.16856

中文摘要

去中心化借贷缺乏征信机构：借款人的还款能力必须完全从公开的链上活动推断，无需收入验证或负债记录。本文提出zLend，一个已部署的现金流承保框架，从原始代币转账重建钱包的每日余额历史并从中推导短期还款能力信号。每个钱包进行两次重建：一次限制于固定稳定币篮子，一次覆盖所有可替代转账——前提是钱包的总代币持有量与其流动、可支出余额是不同的量，混淆它们会错误定价风险。从每个序列我们推导：针对固定贷款规模的流动性覆盖率、现金流波动性和规律性、改编自量化金融的回撤恢复统计量，以及从转账时间识别类工资支付节奏的经常性交易对手检测器。两种视图然后比较：总持有量大但稳定币储备很少覆盖贷款规模的钱包被标记为流动性错配。zLend已投入生产，通过第三方API集成提供真实借贷决策支持。

原文摘要

Decentralized lending lacks a credit bureau: a borrower's capacity to repay must be inferred entirely from public on-chain activity, without income verification or a liability record. This paper presents zLend, a deployed cash-flow underwriting framework that reconstructs a wallet's daily balance history from raw token transfers and derives short-duration repayment-capacity signals from it. The reconstruction is performed twice per wallet, once restricted to a fixed stablecoin basket and once over all fungible transfers, on the premise that a wallet's total token holdings and its liquid, spendable balance are distinct quantities whose conflation misprices risk. From each series we derive liquidity coverage against a fixed loan size, cash-flow volatility and regularity, a drawdown-and-recov...

--- *自动采集于 2026-08-19*

#论文 #arXiv #ML #小凯