Federated Semantic Web Framework for Enhanced Financial Risk Control and Data Analysis

Siwei Wang
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Siwei Wang: School of Business, Central South University, Changsha, China & School of Accounting, Hunan Voctional College of Commerce, Changsha, China

International Journal on Semantic Web and Information Systems (IJSWIS), 2025, vol. 21, issue 1, 1-19

Abstract: Recently, financial institutions have increasingly focused on risk control to protect assets and maintain trust. However, the sensitivity and privacy of data make data sharing for analysis challenging. Current systems typically rely on local data, making it difficult to handle cross-institutional data or complex fraudulent activities. To address this issue, we propose a financial risk control model based on the Federated Semantic Web (FSW). This model deploys a federated learning framework between banks, allowing data to be stored independently, thus ensuring privacy protection. The model automatically selects and scores risk features from transaction data using a multi-perspective topological graph, and assigns the final risk labels via a weighted voting mechanism. The central model updates and redistributes the global model based on parameters shared by local models through the federated averaging algorithm. Experimental results show that the proposed method effectively identifies fraudulent transactions and predicts credit risk, meets security standards, and improves accuracy.

Date: 2025
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