Integrating Feature Attention and Temporal Modeling for Collaborative Financial Risk Assessment

2025-08-12

AI Summary

To address the challenge of jointly ensuring data privacy and enabling collaborative modeling in cross-institutional financial risk analysis, this paper proposes a federated learning framework integrating feature-wise attention mechanisms with temporal modeling. The framework employs distributed optimization and differential privacy noise injection to enable multi-party collaborative modeling without sharing raw data, thereby preserving data sovereignty. Crucially, it innovatively embeds attention mechanisms into the federated temporal architecture to enhance detection of critical risk features and improve cross-market generalization. Experimental results demonstrate that the proposed method significantly outperforms both centralized models and state-of-the-art federated learning baselines in communication efficiency, prediction accuracy, systemic risk identification precision, and cross-market transferability. The approach exhibits strong practicality and deployment potential for real-world financial risk monitoring systems.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Natural Language Processing: Safety and Robustness
• Computer Vision: Multi-modal Vision

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

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.

Problem

• Enables secure cross-institution financial risk assessment without data sharing
• Integrates feature attention and temporal modeling for improved risk identification
• Uses federated learning with privacy protection for collaborative modeling

Innovation

• Federated learning for cross-institution risk assessment
• Feature attention and temporal modeling integration
• Differential privacy with noise injection protection