In the context of a progressively intricate and uncertain global economic landscape, the credit risk businesses encounter is intensifying. This study seeks to analyze whether intelligent transformation, a significant trend in current organization development, might serve as a novel method for mitigating credit risk. We employ panel data from 1533 listed enterprises in China’s manufacturing sector to investigate how intelligent transformation influences credit risk empirically. This research indicates that intelligent transformation can mitigate business credit risk. The production, management, and financing effects are the primary mechanisms via which intelligent transformation mitigates credit risk. Heterogeneity analysis indicated that the credit risk reduction effect of the intelligent transformation of traditional manufacturing firms surpassed that of intelligent manufacturing enterprises. In contrast to high-growth firms, low-growth enterprises exhibited more robust credit risk mitigation benefits from intelligent transformation. Subsequent analysis indicated that enhancing supply chain finance can facilitate intelligent transformation and, hence, more effectively mitigate credit risk.

The influence of digital transformation (DT) on corporate credit risk is experimentally investigated in this article from 2015 to 2021 using a sample of listed Chinese intelligent manufacturing enterprises (IMEs). The results indicate a nonlinear relationship between credit risk and DT. Once DT reaches a certain point, credit risk decreases. Overcommitment to DT raises credit risk. Several robustness tests confirm that the conclusion is still valid. DT impacts credit risk through three different mechanisms, according to mechanism analysis: the finance effect, and the innovation effect. According to heterogeneity analysis, the nonlinear impact of DT on credit risk is more pronounced in IMEs with lower governance levels and higher productivity. This study enables us to understand DT 's economic consequences. This paper provides empirical insights for IMEs to develop DT and prevent credit risk rationally.