End-To-End Automation: Impact of Programmable Credit Decisions on SME Cash Conversion Cycles

Agasthya Khurana

Abstract: This paper examines the impact of end-to-end automation-where AI underwriting is integrated with smart-contract-based settlement- on the cash conversion cycles of small and medium enterprises (SMEs). Through a conceptual framework and a field-experiment design with a supply-chain finance (SCF) provider, the study quantifies the effects of programmable credit decisions and automated settlement on time-to-cash, days-sales-outstanding (DSO), days-payable-outstanding (DPO), inventory days, reconciliation effort, error rates and counterparty disputes. The findings are expected to shed light on whether AI+ smart- contract automation materially shortens the cash conversion cycle (CCC), reduces operational frictions and lowers error/risk rates, and how these effects vary by firm characteristics and supply-chain complexity. The paper also discusses governance, data-privacy and fairness considerations for deploying automated credit decisions in SCF workflows.

Keywords: programmable credit, smart contracts, supply-chain finance, AI underwriting, cash conversion cycle, SME financing, time-to-cash, reconciliation, field experiment

How to Cite?: Agasthya Khurana, "End-To-End Automation: Impact of Programmable Credit Decisions on SME Cash Conversion Cycles", Volume 14 Issue 12, December 2025, International Journal of Science and Research (IJSR), Pages: 1730-1733, https://www.ijsr.net/getabstract.php?paperid=SR251107120532, DOI: https://dx.doi.org/10.21275/SR251107120532