Integrating Feature Attention and Temporal Modeling for Collaborative Financial Risk Assessment
It addresses data privacy and collaborative modeling challenges for financial institutions, offering a secure solution, but it is incremental as it builds on federated learning with added mechanisms.
The paper tackles collaborative financial risk assessment across institutions without sharing raw data by proposing a federated learning framework with feature attention and temporal modeling, and it shows the model outperforms centralized and existing federated methods in accuracy, efficiency, and risk detection.
This paper addresses the challenges of data privacy and collaborative modeling in cross-institution financial risk analysis. It proposes a risk assessment framework based on federated learning. Without sharing raw data, the method enables joint modeling and risk identification across multiple institutions. This is achieved by incorporating a feature attention mechanism and temporal modeling structure. Specifically, the model adopts a distributed optimization strategy. Each financial institution trains a local sub-model. The model parameters are protected using differential privacy and noise injection before being uploaded. A central server then aggregates these parameters to generate a global model. This global model is used for systemic risk identification. To validate the effectiveness of the proposed method, multiple experiments are conducted. These evaluate communication efficiency, model accuracy, systemic risk detection, and cross-market generalization. The results show that the proposed model outperforms both traditional centralized methods and existing federated learning variants across all evaluation metrics. It demonstrates strong modeling capabilities and practical value in sensitive financial environments. The method enhances the scope and efficiency of risk identification while preserving data sovereignty. It offers a secure and efficient solution for intelligent financial risk analysis.