zLend: A Dual-Scope Cash-Flow Reconstruction Framework for On-Chain Credit Underwriting
2026-08-17 • Machine Learning
Machine Learning
AI summaryⓘ
The authors present zLend, a tool that helps decide if someone can repay a loan using only their public cryptocurrency wallet activity, since there’s no traditional credit check. They analyze daily wallet balances in two ways: looking just at stablecoins and looking at all tokens, to better understand spending power versus total wealth. From this, they calculate measures like liquidity coverage and payment regularity to estimate repayment ability. The system is tested carefully for accuracy and is already used in real lending decisions through APIs.
decentralized lendingcredit bureaustablecoinwallet balanceliquidity coveragecash flowrepayment capacitydrawdown statisticcryptocurrencyAPI integration
Authors
Girish G N, Ashutosh Sahoo, Akshay SP, Gurukiran S, Dhanashekar Kandaswamy
Abstract
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-recovery statistic adapted from quantitative finance, and a recurring-counterparty detector that identifies salary-like payment cadence from transfer timing alone. The two views are then compared: a wallet with large aggregate holdings whose stablecoin reserve rarely covers the loan size is flagged as a liquidity mismatch irrespective of total wealth. We specify the pipeline formally, document the golden-master methodology used to verify a cross-language production migration to numerical tolerance 1e-9, and characterize the tier function's parameter sensitivity with an independent reimplementation validated to exact agreement (78 of 78 field assertions) against the deployed system's reference fixtures. Tier assignment is governed predominantly by the reference loan size, with four of six reference wallets changing tier across loan sizes from USD 10 to USD 25,000; the drawdown and coverage criteria bind on disjoint wallets, so neither subsumes the other; and no criterion in the tier rule is inert. zLend is deployed in production, informing real lending decisions via third-party API integrations.