Energy Consumption Graph Inference for Financing Risk Prediction in Industrial Enterprises
Abstract
Energy cost financing is increasingly used by industrial enterprises to manage electricity, gas, steam, and fuel expenses during periods of high production demand or cash-flow pressure. However, abnormal energy consumption, declining production intensity, delayed utility payments, and unstable downstream orders may signal future repayment risk before conventional financial indicators change. This study proposes an energy consumption graph inference model for financing risk prediction in industrial enterprises. The model constructs a heterogeneous graph linking enterprises, utility providers, production facilities, electricity meters, gas accounts, invoices, financing contracts, downstream customers, and overdue payment records. A graph neural network is used to encode enterprise production-energy dependency, while a knowledge reasoning module identifies risk chains involving shrinking energy use, abnormal peak-valley consumption, repeated utility arrears, customer-order decline, and concentrated financing exposure. Experiments are conducted on an industrial energy finance dataset containing 41,600 enterprises, 126,000 utility accounts, 2.84 million monthly energy bills, 760,000 customer-supplier relations, 68,000 financing contracts, and 8,420 confirmed repayment-risk cases over 48 months. Compared with a financial-ratio baseline, the proposed model shortens median warning time before repayment deterioration from 84 days to 29 days. Graph inference identifies 5,930 energy-production risk chains and 2,170 enterprises with simultaneous order decline and utility arrears. Risk-path aggregation reduces 13,800 raw enterprise alerts to 3,640 investigation cases. Full monthly portfolio assessment is completed in 15.2 minutes, with a median inference latency of 41 ms per enterprise node. These results indicate that energy consumption graph inference can improve industrial financing risk prediction by connecting production activity, utility payment behavior, and enterprise relationship networks.
