RAFN: A risk-aware feature network for identifying risk factors in supply chain finance

As supply chain finance businesses expand, traditional risk assessment systems, which rely heavily on manual processes and static rule-based frameworks, are increasingly unable to keep up with the complexity and dynamism of modern risk patterns. This often leads to delayed responses and inefficiencies in risk management. To address key challenges such as difficulties in integrating heterogeneous data, low detection rates for hidden risks, and limited ability to capture dynamic risk patterns, this paper introduces a novel Risk-Aware Feature Network (RAFN) driven by an adaptive attention mechanism. The RAFN model is designed with a dual-channel architecture to process numerical and categorical data separately, employs gated linear units to dynamically merge heterogeneous data streams, and incorporates a multi-head attention mechanism with dynamic coefficients to focus on risk-sensitive features adaptively. Experiments conducted on both public and proprietary datasets show that RAFN outperforms mainstream algorithms, achieving a 1.73%-5.81% improvement in accuracy, recall, and F1-score, while maintaining a strong balance between specificity and recall. Furthermore, this study proposes a closed-loop risk management framework based on RAFN, which integrates “smart contract triggering, off-chain model evaluation, and on-chain consensus validation.” This approach offers an efficient technical solution to break down data silos and enhance the precision of risk identification in supply chain finance, paving the way for more effective and reliable risk control systems.