Trust-Aware Enterprise Credit Risk Prediction via Variational Autoencoder in Supply Chain Finance

The growing complexity of transaction networks, dynamic enterprise interactions, and rising financial uncertainties in modern supply chains have made accurate credit risk assessment for small and medium-sized enterprises (SMEs) in supply chain finance (SCF) a critical priority. However, traditional credit prediction models struggle to capture meaningful latent risk patterns and dynamic risk propagation within supply chain networks, limiting their effectiveness, robustness, and interpretability in supporting trustworthy decision-making in SCF ecosystems. To this end, we propose TrustVAE , a novel end-to-end generative paradigm that jointly models iterative node aggregation, trust-aware message passing, and spatiotemporal dynamics for credit risk assessment in SCF. Specifically, TrustVAE leverages the Iterative Latent Risk Aggregation (ILRA) module to extract latent risk representations from raw enterprise data in an unsupervised manner through deep nonlinear transformations and iterative clustering, capturing inherent structural properties that extend beyond the static financial indicators used in conventional models. Meanwhile, the Bounded Graph Attention Network (BGAN) module integrates trust-aware bounded confidence into graph-based risk propagation, enhancing the fidelity of diffusion modeling by suppressing risk transmission across low-trust enterprise edges. Furthermore, TrustVAE introduces the Temporal Graph Autoregressive Flow (TGAF) module, which couples temporal dependencies with autoregressive flow modeling to characterize credit risk evolution across multi-scale temporal horizons (e.g., daily, weekly, and monthly), jointly capturing dynamic patterns in both temporal and graph-structured domains. Extensive experiments on real-world datasets demonstrate that TrustVAE outperforms existing competitors, achieving average absolute improvements of 1.59%, 3.17%, 1.93%, and 2.11% in ROC-AUC, AR, Accuracy, and F1 Score, respectively. The unified design of TrustVAE strengthens the practical foundation of information systems in addressing credit risk within data-intensive, networked environments, offering novel insights into generative modeling for credit risk prediction.