Export Order Financing Risk Prediction Using Cross-Border Trade Knowledge Graphs

Export order financing provides working capital for industrial enterprises before goods are delivered and payments are received. However, this financing mode is exposed to risks from unstable overseas buyers, delayed customs clearance, abnormal logistics records, exchange-rate fluctuation, export-document inconsistency, and weak supplier fulfillment capacity. Traditional credit assessment models mainly focus on the exporter’s financial statements and often fail to verify the reliability of cross-border trade relationships. This study proposes a cross-border trade knowledge graph model for export order financing risk prediction. The model links exporters, overseas buyers, purchase orders, customs declarations, shipping records, logistics providers, letters of credit, exchange-rate movements, tax rebate records, and repayment outcomes. A graph neural encoder learns exporter-buyer dependency, while a knowledge inference module identifies risk chains involving repeated order cancellation, delayed shipment, abnormal customs declaration, buyer credit deterioration, and foreign-exchange exposure. Experiments are conducted on an export finance dataset containing 26,400 industrial exporters, 91,000 overseas buyers, 1.72 million export orders, 640,000 customs declarations, 480,000 shipping records, and 6,920 confirmed financing-risk cases over 38 months. The proposed method shortens median warning time before repayment deterioration from 66 days to 23 days compared with an exporter-level credit scoring baseline. Graph reasoning identifies 8,760 cross-border trade risk paths and 2,180 buyer-concentration risk chains. The system reduces manual document review from 24,300 order batches to 5,120 graph-linked investigation cases. Full monthly assessment is completed in 12.1 minutes, with a median inference latency of 44 ms per exporter node. These results indicate that cross-border trade knowledge graphs can improve export order financing risk prediction by connecting credit risk with order authenticity, logistics evidence, and buyer-side repayment uncertainty.

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