The Impact of Regional Financial Resources on the Credit Risk of State-Owned Enterprises Under AI Empowerment: Based on Correlation Analysis and PCA Model

Local state-owned enterprises (SOEs) face a growing debt sustainability issue, and preventing their credit defaults is key to curbing systemic financial risks. Regional financial resources, mainly from urban commercial banks, are their primary financial support. Using 2021-2023 panel data of urban commercial banks across 31 Chinese provinces/municipalities, this study leverages AI (optimizing multi-dimensional data processing and correlation analysis accuracy) and focuses on three indicators-urban commercial banks’ financial aggregates, local economic development, local SOE bond credit spreads-to explore the link between regional financial resources and local SOEs’ credit risk. Methodologically, it combines AI-assisted data multiprocessing e.g., abnormal data cleaning missing value supplementation, correlation analysis, and Principal Component Analysis (PCA): correlation analysis identifies initial associations, while PCA extracts core factors to clarify key paths. Results: 1) Post AI verification, regional urban commercial banks’ asset scale and capital adequacy ratio show stable, significant negative correlations with local SOE credit spreads (lower spreads = lower risk); 2) Their asset quality (via non-performing loan ratio) and loan support for local SOE bonds have significant positive correlations, suggesting poor quality and excessive loan concentration may raise local SOEs’ credit risk.