Inventory Pledge Risk Prediction for Industrial Enterprises Using Warehouse Knowledge Graph Inference

Inventory pledge financing is widely used by industrial enterprises to obtain short-term liquidity through raw materials, semi-finished goods, finished products, and warehouse receipts. However, this financing mode is exposed to risks from duplicated pledges, false warehouse records, unstable commodity prices, inventory depreciation, abnormal turnover, and weak borrower repayment capacity. This study proposes a warehouse knowledge graph inference model for inventory pledge risk prediction in industrial enterprises. The model links borrowers, pledged inventories, warehouses, warehouse receipts, logistics providers, valuation agencies, insurance records, purchase contracts, sales invoices, commodity prices, and repayment outcomes. A graph neural encoder is used to learn borrower-inventory dependency, while a knowledge reasoning module identifies hidden risk chains involving repeated pledge registration, inventory-location inconsistency, abnormal valuation fluctuation, delayed warehouse inspection, and declining downstream sales. Experiments are conducted on an industrial inventory financing dataset containing 24,800 borrowing enterprises, 680 warehouses, 1.36 million inventory records, 92,000 pledge contracts, 410,000 logistics records, 58,000 insurance entries, and 6,740 confirmed pledge-risk cases over 36 months. The proposed method shortens median warning time before pledge deterioration from 64 days to 21 days compared with a collateral-value scoring baseline. Graph inference identifies 7,860 suspicious inventory-risk chains and 1,690 duplicated pledge structures. The system reduces manual collateral review from 22,400 warehouse batches to 4,960 graph-linked investigation cases. Full monthly assessment is completed in 10.8 minutes, with a median inference latency of 38 ms per pledge node. The results show that warehouse knowledge graph inference can improve inventory pledge risk prediction by connecting collateral authenticity, logistics evidence, price movement, and enterprise repayment behavior.

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