Nataliia Kolodnenko

This article highlights the results of a study investigating whether the growth of syndicated loan activity among US commercial banks was driven by measurable operational cost savings through blockchain-powered back-office automation. Quarterly data from Q1 2010 to Q4 2024 on syndicated loan stocks, commercial and industrial loans, real GDP, bank assets, and non-interest expenses were obtained from the Federal Reserve System’s FRED database. A dummy variable was applied after 2016 to denote the implementation of the first production-level Distributed Ledger Technology (DLT) pilots. Using the Autoregressive Distributed Lag Model (ARDL) bounds testing approach, evidence of cointegration is found and long-run elasticity is estimated: a steady 1% increase in the volume of syndicated loans reduces the operating expense ratio by 0.147%, which means that almost doubling the volume of loans in the resulting sample leads to approximately 15% structural reduction in the burden on banks’ back offices. The associated error correction model gives a short-run elasticity of –0.276 (i.e., a 1% quarterly shock to loan volume reduces expenses by 0.276 p.p.) and a 47% correction rate to a new equilibrium. Diagnostic tests confirm the absence of sequential correlation and resistance to heteroscedasticity by White’s standard errors. System-wide process improvements were evaluated by examining Hyperledger Fabric’s permissioned channel blockchain, smart contract automation, and multi-signature approval policies, which together simplify Know Your Customer (KYC) document workflows and settlement processes. The findings provide empirical evidence that enterprise DLT platforms deliver significant cost reductions for syndicated loan transactions, with implications for bank, fintech, and regulatory strategies.