Ownership Knowledge Graphs for Post-Merger Financing Risk Assessment in Industrial Groups

Industrial mergers and acquisitions often involve complex ownership changes, debt restructuring, cross-border suppliers, asset transfers, related-party transactions, and post-merger integration pressure. Financing risk may rise after a transaction when the acquiring group inherits hidden liabilities, weak subsidiaries, guarantee obligations, or unstable supplier networks. This study proposes an ownership knowledge graph model for post-merger financing risk assessment in industrial groups. The model integrates acquirers, target firms, subsidiaries, shareholders, executives, suppliers, customers, creditors, guarantee contracts, asset-transfer records, loan agreements, and litigation events into a unified enterprise graph. A graph neural network is used to encode post-merger structural changes, while a rule-guided inference module detects hidden risk chains involving debt concentration, related-party guarantees, supplier disruption, and subsidiary credit deterioration. The empirical dataset contains 6,420 industrial merger events, 38,600 related enterprises, 214,000 ownership-change records, 96,000 loan agreements, 58,000 guarantee contracts, 740,000 supplier-customer links, and 5,280 post-merger financing-risk events over 60 months. Compared with a transaction-level financial scoring model, the proposed approach shortens median risk-identification time from 118 days to 49 days after merger completion. It discovers 3,760 hidden ownership-risk paths and 1,540 subsidiary-level debt transmission chains. Portfolio simulation shows that graph-informed monitoring reduces expected post-merger overdue exposure by 96 million RMB during the validation window. Full quarterly assessment of all merger groups is completed in 10.4 minutes. The results demonstrate that ownership knowledge graph reasoning can support more interpretable financing risk assessment for industrial groups after mergers and acquisitions.

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