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Company financial risk is ubiquitous and early risk assessment for listedcompanies can avoid considerable losses. Traditional methods mainly focus onthe financial statements of companies and lack the complex relationships amongthem. However, the financial statements are often biased and lagged, making itdifficult to identify risks accurately and timely. To address the challenges,we redefine the problem as \textbf{company financial risk assessment ontribe-style graph} by taking each listed company and its shareholders as atribe and leveraging financial news to build inter-tribe connections. Suchtribe-style graphs present different patterns to distinguish risky companiesfrom normal ones. However, most nodes in the tribe-style graph lack attributes,making it difficult to directly adopt existing graph learning methods (e.g.,Graph Neural Networks(GNNs)). In this paper, we propose a novel HierarchicalGraph Neural Network (TH-GNN) for Tribe-style graphs via two levels, with thefirst level to encode the structure pattern of the tribes with contrastivelearning, and the second level to diffuse information based on the inter-triberelations, achieving effective and efficient risk assessment. Extensiveexperiments on the real-world company dataset show that our method achievessignificant improvements on financial risk assessment over previous competingmethods. Also, the extensive ablation studies and visualization comprehensivelyshow the effectiveness of our method.

Company-as-Tribe: Company Financial Risk Assessment on Tribe-Style Graph with Hierarchical Graph Neural Networks