Paper Title: Research on the impact mechanism of AI-driven supply chain creditworthiness assessment on commercial banks’ credit policies for SMEs
Authors: Feng Wu, Nusanee Meekaewkunchom, Chaiyawit Muangmee, Tatchapong Sattabut
Corresponding Author: Nusanee Meekaewkunchom (Nusanee.me@bsru.ac.th)/Thailand
Abstract
This study investigates how AI-driven supply chain creditworthiness assessment transforms commercial banks’ credit policies for small and medium-sized enterprises (SMEs), addressing the persistent SME financing gap through technological innovation. Using structural equation modeling, we analyzed data from 360 commercial banking professionals across China to test five hypotheses grounded in information asymmetry theory, relationship lending theory, group lending theory, and supply chain finance theory. SME credit status and core enterprise influence significantly impact bank credit policies (β = 0.285 and β = 0.317, p < 0.001), with AI-enhanced bank cognition serving as a partial mediator (indirect effects: β = 0.167 and β = 0.193, p < 0.001). Critically, AI assessment accuracy moderates these relationships, with higher-accuracy systems demonstrating stronger policy effects (β = 0.124 and β = 0.138, p < 0.001). AI fundamentally transforms SME credit evaluation by enhancing risk assessment accuracy, effectively leveraging supply chain relationships, and augmenting banks’ cognitive capabilities. The moderating role of AI precision emphasizes the importance of technological sophistication for maximizing benefits. This research provides empirical evidence that AI-powered supply chain finance offers a viable solution to global SME financing constraints while maintaining robust risk management standards.
Keywords
AI-driven assessment, Supply chain finance, SME credit, Commercial banks, Creditworthiness evaluation
