Environmental Compliance Graph Reasoning for Financing Risk Prediction in Industrial Enterprises

Environmental compliance has become an important factor in industrial enterprise financing, especially for firms in chemicals, metallurgy, energy, building materials, and heavy manufacturing. Environmental penalties, emission violations, production restrictions, delayed rectification, and pollution-control investment pressure may affect cash flow, credit access, and repayment stability. This study proposes an environmental compliance graph reasoning model for financing risk prediction in industrial enterprises. The model constructs a heterogeneous knowledge graph linking enterprises, environmental penalties, emission permits, production facilities, pollution-control equipment, bank loans, supplier relationships, customer orders, government subsidies, and repayment outcomes. A graph neural network is used to encode enterprise compliance and financing dependency, while a knowledge reasoning module identifies risk chains involving repeated penalties, suspended production, delayed rectification, subsidy withdrawal, supplier disruption, and loan repayment pressure. Experiments are conducted on an industrial compliance-finance dataset containing 57,300 enterprises, 138,000 environmental inspection records, 42,600 penalty events, 96,000 emission-permit records, 710,000 supplier-customer links, 52,400 loan contracts, and 7,860 confirmed financing-risk cases over 50 months. The proposed model shortens median financing-risk warning time from 82 days to 30 days compared with a financial-indicator baseline. Graph reasoning identifies 6,240 compliance-driven risk chains and 2,080 enterprises affected by both environmental penalties and order contraction. The system consolidates 15,700 raw enterprise alerts into 3,890 investigation cases through compliance-path aggregation. Full monthly assessment is completed in 13.9 minutes, with a median inference latency of 47 ms per enterprise node. The results indicate that environmental compliance graph reasoning can improve industrial financing risk prediction by connecting regulatory events, operational constraints, and enterprise credit behavior.

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