🤖 AI Summary
This study addresses the challenge of corporate risk monitoring, which is often hindered by data sparsity, imbalanced distributions, and visibility bias—where unrecorded events do not necessarily imply absence of risk. The problem is formulated as a positive-unlabeled (PU) node classification task on an equity ownership graph. To tackle potential contamination from latent positive samples in the unlabeled set, the authors propose a GCNII-based framework that integrates relation-aware message passing with non-negative PU learning. By constructing a corporate ownership graph and incorporating relation-specific message propagation mechanisms, the model achieves significantly superior performance in prospective risk assessment compared to both non-graph and baseline graph-based methods, demonstrating robust predictive capability even for firms with no prior risk records.
📝 Abstract
The monitoring of business conduct risk is hindered by sparse, uneven, and visibility-biased data. Prior studies show that business conduct risk information and media coverage propagate through supply chain, peer, and corporate structure networks, yet incident records remain incomplete for many firms. As a result, the absence of reported events could reflect limited coverage rather than the absence of underlying business conduct risk. This paper examines whether inter-firm relationships can improve the prediction of future recorded conduct related incidents, particularly among firms with limited prior visibility. We formulate the task as Positive--Unlabeled node classification on a corporate ownership graph, where firms with recorded incidents are treated as labeled positives and firms without recorded incidents remain unlabeled. We then propose a visibility- and relation-aware GCNII framework that combines relation specific message passing with non-negative Positive--Unlabeled learning to account for positive contamination in the unlabeled set. In a forward-looking evaluation, the proposed approach achieved the strongest observed ranking performance relative to non-graph- and simple graph-based benchmarks. The results further show that graph-based inference retains its predictive value among firms without prior recorded incidents. These findings demonstrate the value of inter-firm relational structure as a complementary source of information for extending risk prioritization