Receivables Pledge Risk Scoring Through Transaction Knowledge Graph Inference

Receivables pledge financing provides liquidity for industrial enterprises, but it also introduces risks from false trade backgrounds, duplicated pledges, delayed payments, debtor concentration, and weak buyer credit. Traditional risk assessment mainly examines the borrowing enterprise and often ignores the transaction network behind receivables. This study develops a receivables pledge risk scoring method based on transaction knowledge graph inference. The proposed method builds a knowledge graph that links borrowers, debtors, invoices, contracts, logistics records, payment histories, tax declarations, pledge registrations, and guarantee relationships. A relation-aware graph neural network is used to encode transaction credibility and debtor repayment dependency. A rule-guided inference module detects suspicious receivable chains, including repeated invoice pledging, circular trade, abnormal payment delay, and buyer-credit deterioration. The empirical dataset contains 28,600 borrowing enterprises, 96,000 debtor firms, 1.84 million invoices, 420,000 logistics records, 73,000 pledge contracts, and 8,950 confirmed pledge-risk cases collected over 30 months. The model identifies 11,300 suspicious receivable chains and 2,740 duplicated or circular pledge structures. Compared with a borrower-only credit scoring model, the proposed method shortens median warning time before pledge default from 57 days to 19 days. It reduces manual document review from 31,000 invoice batches to 6,480 graph-linked investigation cases. The top-ranked risk explanations mainly involve debtor payment delay, inconsistent logistics evidence, repeated pledge registration, and excessive receivable concentration. These findings suggest that transaction knowledge graph inference can improve receivables pledge risk assessment by connecting financing risk with real trade evidence and inter-enterprise repayment dependency.

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