Function-Driven Knowledge-Enhanced Neural Modeling for Intelligent Financial Risk Identification

This paper proposes a financial risk identification framework based on the Knowledge-enhanced Neural Network (KAN) to address the challenges of highly concealed risk behaviors, complex data structures, and sparse anomalies in the digital financial environment. The method is designed around function-level prior expression and structure-aware mechanisms. It introduces a differentiable basis function encoder to perform structured mapping of multimodal transaction data. A structure-aware transformation stack is then used to extract high-order behavioral interaction patterns. A high-order composite function module is applied to achieve the deep expression of risk factors. To enhance the stability of risk discrimination in dynamic environments, a contrastive consistency regularizer is further designed to guide the model in maintaining semantic alignment and structural robustness across multimodal representations. The proposed method demonstrates significant advantages in risk identification accuracy and robustness across several key performance metrics. It effectively alleviates the limitations of traditional neural models in heterogeneous financial scenarios, especially their weak generalization and delayed response to low-intensity risk signals. Through systematic model design and comprehensive metric evaluation, this work validates the effectiveness of KAN in enhancing risk pattern characterization across structural representation, layered composition, and contrastive reasoning. It provides both theoretical support and algorithmic foundations for building interpretable, controllable, and high-performance intelligent financial risk identification systems.